US20140180692A1 - Intent mining via analysis of utterances - Google Patents
Intent mining via analysis of utterances Download PDFInfo
- Publication number
- US20140180692A1 US20140180692A1 US14/184,379 US201414184379A US2014180692A1 US 20140180692 A1 US20140180692 A1 US 20140180692A1 US 201414184379 A US201414184379 A US 201414184379A US 2014180692 A1 US2014180692 A1 US 2014180692A1
- Authority
- US
- United States
- Prior art keywords
- utterance
- word
- words
- utterances
- candidate
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Abandoned
Links
Images
Classifications
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L15/00—Speech recognition
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/60—Information retrieval; Database structures therefor; File system structures therefor of audio data
- G06F16/68—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
- G06F16/683—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/20—Natural language analysis
- G06F40/205—Parsing
- G06F40/211—Syntactic parsing, e.g. based on context-free grammar [CFG] or unification grammars
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/30—Semantic analysis
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L15/00—Speech recognition
- G10L15/08—Speech classification or search
- G10L15/18—Speech classification or search using natural language modelling
- G10L15/1822—Parsing for meaning understanding
Definitions
- Speech recognition systems are highly complex and operate by matching an acoustic signature of an utterance with acoustic signatures of words in a language model.
- a microphone first converts a received acoustic signature of an uttered word into an electrical signal.
- An A/D (analog-to-digital) converter is typically used to convert the electrical signal into a digital representation of the uttered word.
- a digital signal processor converts the captured electrical signal from the time domain to the frequency domain.
- the digital signal processor breaks down the utterance into its spectral components.
- the amplitude or intensity of the digital signal at various frequencies and temporal locations are then compared to a language model to determine the one or more words that were uttered.
- a conventional speech recognition system can receive and convert an utterance into respective text.
- a conventional speech recognition system can be configured to classify the utterance based on a key word in the utterance.
- Conventional speech recognition systems can suffer from drawbacks. For example, conventional speech recognition typically analyze an utterance for presence of words that indicate a likely class to which the utterance belongs. However, the accuracy of the classification can be quite low because an utterance may include many words, making the intended meaning of the utterance difficult to determine.
- Embodiments herein deviate with respect to conventional speech recognition systems to provide accurate speech utterance classification.
- one embodiment herein includes a speech processing system to identify one or more intended meanings of a received utterance based on word groupings derived from words detected in the utterance.
- the speech processing system tags the utterance with respective one or more tags indicative of one or more classes believed to represent an intended meaning of the utterance.
- a speech processing system can include a syntactic parser, a word extractor, word pattern rules, and an analyzer. To identify an intended general meaning of the utterance, the speech processing system performs a number of tasks on a received utterance.
- the syntactic parser of the speech processing system parses a received utterance to identify syntactic relationships amongst words in the utterance.
- the word extractor creates sets of words using words in the utterance based at least in part on the syntactic relationships identified by the parser.
- the word extractor can utilize the word pattern rules to identify groupings of related words in the utterance that most likely represent an intended meaning of the utterance.
- the pattern rules specify which type and/or location of related words in the utterance that are to be used to create the sets of words. Accordingly, embodiments herein can include creating the sets of words can include utilizing the identified syntactic relationships of words to identify groupings of related words in the utterance; and applying a set of pattern rules or word extraction rules to the identified syntactic relationships to identify types and/or locations of words in the utterance to create the sets of words.
- an analyzer in the speech processing system maps each set of the sets of words produced by the word extractor to a respective candidate intent value to produce a list of candidate intent values for the utterance.
- the received utterance can be mapped multiple candidate intent values.
- a candidate intent value is a possible intended meaning of the utterance.
- An utterance can have one or more intended meanings; the speech processing system can be configured to identify one or more most likely intended meanings of the utterance under test.
- the analyzer is configured to select, from the list of possible intended meanings of the utterance, a particular candidate intent value as being representative of the intent (i.e., intended meaning) of the utterance.
- the speech processing system can be configured to maintain statistical information for a pool of previously received utterances to determine the meaning of future utterances.
- the statistical information can indicate a frequency of receiving utterances of different intent types.
- the statistical information can be updated over time to reflect that a most recently received and analyzed utterance was assigned the particular candidate intent value as discussed above.
- Selection of particular intent value amongst a group of possible intents for an utterance can be based on selection criteria employed by the analyzer. For example, in one embodiment, the analyzer identifies a frequency of occurrence that utterances in a pool of previously received utterances were of a same intent type as that of a first candidate intent value for the newly received utterance; the analyzer also identifies a frequency of occurrence that utterances in the pool of previously received utterances were of a same intent type as that of the second candidate intent value for the newly received utterance; and so on. As previously discussed, the analyzer can identify multiple possible classes in which to categorizes or classify the received utterance.
- the analyzer selects the a particular candidate intent value for assigning to the utterance depending on which of the possible candidate intent values (e.g., first candidate intent value, second candidate intent value, etc.) occurred more often in the pool for the previously received utterances.
- the analyzer can perform a frequency analysis and then sort the candidate meanings.
- the selected candidate value indicates a likely dominant subject matter or theme of the utterance.
- the speech processing system includes a set of tags or labels.
- a tagging resource (such as the analyzer or other suitable resource) identifies an appropriate tag that is representative of the intent value (or selected meaning) selected for the utterance. The tagging resource then tags the utterance with the appropriate tag to indicate a likely dominant subject matter intended by words in the received utterance. Accordingly, embodiments herein can include classifying one or more received utterances using tags.
- embodiments herein can include a configuration of one or more computerized devices, workstations, handheld or laptop computers, or the like to carry out and/or support any or all of the method operations disclosed herein.
- one or more computerized devices or processors can be programmed and/or configured to operate as explained herein to carry out different embodiments of the invention.
- One such embodiment comprises a computer program product including a non-transitory computer-readable storage medium on which software instructions are encoded for subsequent execution.
- the instructions when executed in a computerized device having a processor, program and/or cause the processor to perform the operations disclosed herein.
- Such arrangements are typically provided as software, code, instructions, and/or other data (e.g., data structures) arranged or encoded on a non-transitory computer readable storage medium such as an optical medium (e.g., CD-ROM), floppy disk, hard disk, memory stick, etc., or other a medium such as firmware or microcode in one or more ROM, RAM, PROM, etc., or as an Application Specific Integrated Circuit (ASIC), etc.
- the software or firmware or other such configurations can be installed onto a computerized device to cause the computerized device to perform the techniques explained herein.
- one particular embodiment of the present disclosure is directed to a computer program product that includes a computer readable storage medium having instructions stored thereon for speech recognition such as converting of an utterance to corresponding text.
- the instructions when executed by a processor of a respective computer device, cause the processor to: parse an utterance to identify syntactic relationships amongst words in the utterance; create or group sets of words from the utterance based at least in part on the syntactic relationships; map each set of the sets of words to a respective candidate intent value; produce a list of candidate intent values for the utterance based on the mapping; and select, from the list, a candidate intent value as being representative of an intent of the utterance.
- system, method, apparatus, instructions on computer readable storage media, etc., as discussed herein can be embodied strictly as a software program, as a hybrid of software and hardware, or as hardware alone such as within a processor, or within an operating system or a within a software application.
- Example embodiments of the invention may be implemented within products and/or software applications such as those manufactured by Nuance Communications, Inc., Burlington, Mass., USA.
- FIG. 1 is an example block diagram of a speech processing system to perform intent mining for one or more utterances according to embodiments herein.
- FIG. 2 is an example diagram illustrating a sample utterance and corresponding relationship information generated by a syntactic parser according to embodiments herein.
- FIG. 3 is an example diagram illustrating a sample utterance and corresponding relationship information generated by a syntactic parser according to embodiments herein.
- FIG. 4 is an example diagram illustrating pattern or word extraction rules according to embodiments herein.
- FIG. 5 is an example diagram illustrating a listing of different utterances that have been classified under a representative intent value according to embodiments herein.
- FIG. 6 is an example diagram illustrating a listing of different utterances that have been classified under a representative intent value according to embodiments herein.
- FIG. 7 is an example diagram illustrating a listing of different utterances that have been classified under a representative intent value according to embodiments herein.
- FIG. 8 is an example diagram illustrating a listing of different utterances that have been classified under a representative intent value according to embodiments herein.
- FIG. 9 is an example diagram illustrating sets of words extracted from utterances and corresponding frequency information of occurrence according to embodiments herein.
- FIG. 10 is an example diagram illustrating application of intent mining to an example according to embodiments herein.
- FIG. 11 is an example diagram illustrating intent values (e.g., representative word groupings) assigned to a respective subject matter label according to embodiments herein.
- FIG. 12 is an example diagram illustrating intent values and corresponding assigned subject matter labels according to embodiments herein.
- FIG. 13 is a diagram illustrating an example computer architecture for executing a speech processing system according to embodiments herein.
- FIG. 14 is a flowchart illustrating an example method of implementing a speech processing system according to embodiments herein.
- FIGS. 15 and 16 combine to form a flowchart illustrating an example method of implementing speech processing system according to embodiments herein.
- a speech processing system includes a syntactic parser, a word extractor, word extraction rules, and an analyzer.
- the syntactic parser of the speech processing system parses the utterance to identify syntactic relationships amongst words in the utterance.
- the word extractor utilizes the word extraction rules to identify groupings of related words in the utterance that most likely represent an intended meaning of the utterance.
- the analyzer in the speech processing system maps each set of the sets of words produced by the word extractor to a respective candidate intent value to produce a list of candidate intent values for the utterance.
- the candidate intent values represent a possible intended meaning of the utterance.
- the analyzer is configured to select, from the list of candidate intent values (i.e., possible intended meanings) of the utterance, a particular candidate intent value as being representative of a dominant intent of the utterance.
- FIG. 1 is an example diagram illustrating a speech processing system 100 according to embodiments herein.
- speech processing system 100 includes a syntactic parser 115 , word extractor 140 , and an analyzer 150 .
- the speech processing system 100 can receive one or more utterances 110 - 1 , 110 - 2 , 100 - 3 , . . . (collectively, utterances 110 ) in response to a query such as “How may I assist you?”
- one embodiment herein includes a speech processing system 100 to identify one or more intended meanings of a received utterance 110 .
- the speech processing system 100 can tag the utterance with respective one or more tags indicative of one or more classes that appear to best represent an intended meaning of the utterance 110 .
- the speech processing system 110 performs a number of tasks on a received utterance.
- the syntactic parser 115 of the speech processing system 100 converts the utterance to respective text and parses the text in the utterance 110 to produce syntactic relationship information 120 .
- the syntactic relationship information 120 indicates syntactic relationships amongst the text-based words in the utterance 110 .
- the word extractor 140 creates sets of words (e.g., candidate word groupings 145 ) using words in the utterance 110 based at least in part on the syntactic relationship information 120 identified by the syntactic parser 115 .
- the word extractor 140 can utilize the word extraction rules 125 to identify one or more groupings of related words in the utterance that most likely represent an intended meaning of the utterances.
- the word extraction rules 125 includes patterns and/or templates that indicate, for example, which types of words, locations of words, etc., in the utterance will be used to create respective word groupings.
- the word extraction rules 125 specify which type and/or location of related words in the utterances are to be used to create the sets of words for a respective utterance. Accordingly, embodiments herein include creating the sets of words for a respective utterance under test can include utilizing the identified syntactic relationship information 120 to identify groupings of related words in the respective utterance; and applying a set of word extraction rules 125 to the identified syntactic relationships and utterance under test to identify types and/or locations of words in the utterance to create the sets of words.
- an analyzer 150 in the speech processing system 150 maps each set of the sets of words (e.g., candidate word groupings 145 ) produced by the word extractor 140 to a respective candidate intent value to produce a list of candidate intent values for the utterance.
- a candidate intent value is a possible intended meaning of the utterance.
- a candidate word grouping produced by the speech processing system 100 for a respective utterance can include any number of words such as a single word or two or more words.
- the word extractor 140 can produce one or multiple candidate word groupings 145 for each utterance.
- An utterance can have one or more intended meanings; the speech processing system 100 can be configured to identify one or more most likely intended meanings of an utterance under test. If there is only one word grouping and corresponding candidate intent value produced for an utterance under test, then the analyzer can assign the single intended meaning of the single candidate intent value to the utterance.
- the analyzer 150 applies further processing to narrow down the multiple possible intended meanings of the utterance to a at least most likely representative of the utterance. For example, in one embodiment, the analyzer is configured to select, from the list of possible intended meanings of the utterance as indicated by the candidate word groupings 145 , a particular candidate intent value as being representative of the intent (i.e., intended meaning) of the utterance.
- This technique of processing can be applied to each of the received utterances 110 .
- the speech processing system 100 can be configured to classify the received utterances based on intended meaning.
- the speech processing system 10 can be configured to maintain statistical information 160 for a pool of previously received utterances.
- the statistical information 160 can indicate an historical frequency of receiving utterances of different intent types.
- the statistical information 160 can be updated over time to reflect that a most recently received and analyzed utterance was assigned a particular candidate intent value.
- the statistical information 160 can be configured to keep track of how often the speech processing system 100 receives utterances of each particular intent type.
- Selection of an intent value amongst a group of possible intents to assign to a newly received utterance under test can be based on selection criteria employed by the analyzer 150 .
- the analyzer 150 selects the a particular candidate intent value for assigning to the utterance depending on which of the possible candidate intent values (e.g., first candidate intent value, second candidate intent value, etc.) occurred more often in the pool for the previously received utterances as specified by the statistical information 160 .
- the analyzer 150 can perform a frequency analysis and then sort the candidate meanings to perform intent mining.
- the selected candidate value indicates a likely dominant subject matter or theme of the utterance.
- One embodiment includes so-called clustering.
- the speech processing system 100 can include a set of tags for assigning to the utterances.
- a tagging resource (such as the analyzer 150 or other suitable resource) identifies an appropriate tag that is representative of the intent value (or selected meaning) for an utterance. The tagging resource then tags the utterance with the appropriate tag to indicate a likely dominant subject matter intended by words in the received utterance. Accordingly, embodiments herein can include classifying one or more received utterances and then applying tags to identify a respective intended meaning.
- FIG. 2 is an example diagram illustrating a sample utterance and corresponding relationship information 120 according to embodiments herein.
- a Link Grammar parser can be used to parse the received utterance 110 - 1 .
- FIG. 3 is an example diagram illustrating a sample utterance and corresponding relationship information according to embodiments herein.
- FIG. 4 is an example diagram illustrating word extraction rules 125 according to embodiments herein.
- the word extraction rules 125 are used to identify the location of different types of words in a received utterance that are to be used to generate respective candidate word groupings 145 .
- the word extractor 140 applies these rules to produce candidate word groupings 145 .
- the word extractor 140 applies word extraction rule 125 - 1 ( FIG. 4 ) to example utterance 110 - 1 “I need to change my next order.”
- a given utterance can include one candidate word grouping or multiple candidate word groupings.
- FIG. 5 is an example diagram illustrating a listing of different example utterances that have been classified under a respective intent value (e.g., speak_representative) according to embodiments herein. That is, processing of each of the example utterances in FIG. 5 via the speech processing system 100 would produce the candidate word grouping 145 - 1 (e.g., a candidate intent value indicating a possible intended meaning of the utterance) for the respective utterance. For example, during processing, syntactic parser 115 parses a respective utterance to produce corresponding syntactic relationship information 120 . The word extractor 140 then utilizes the respective word extraction rules 125 to identify that the respective utterance includes the respective candidate word grouping 145 - 1 speak_representative.
- a respective intent value e.g., speak_representative
- FIG. 6 is an example diagram illustrating a listing of different utterances that have been classified under a respective intent value (e.g., cancel_delivery) according to embodiments herein. That is, processing of the example utterances 110 in FIG. 6 via the speech processing system 100 would produce the candidate word grouping 145 - 2 (e.g., a candidate intent value indicating a possible intended meaning of the utterance) for the respective utterance. For example, during processing, syntactic parser 115 would parse the utterances to produce corresponding syntactic relationship information. The word extractor 140 then utilizes the respective word extraction rules 125 to identify that each of the utterances 110 as shown include the respective candidate word groupings 145 - 1 .
- a respective intent value e.g., cancel_delivery
- FIG. 7 is an example diagram illustrating a listing of different utterances that have been classified under a respective intent value (e.g., pay_bill) according to embodiments herein. That is, processing of each of the example utterances 110 in FIG. 7 via the speech processing system 100 would produce the candidate word grouping 145 - 3 (e.g., a candidate intent value indicating a possible intended meaning of the utterance) for the respective utterance. During processing, syntactic parser 115 parses a respective utterance to produce corresponding syntactic relationship information. The word extractor 140 then utilizes the respective word extraction rules 125 to identify that the respective utterance includes the respective candidate word grouping 145 - 1 pay_bill.
- a respective intent value e.g., pay_bill
- FIG. 8 is an example diagram illustrating a listing of different utterances that have been classified under a respective intent value (e.g., when_delivery) according to embodiments herein. That is, processing of each of the example utterances 110 in FIG. 8 via the speech processing system 100 would produce the candidate word grouping 145 - 4 (e.g., a candidate intent value indicating a possible intended meaning of the utterance) for the utterance. During processing, syntactic parser 115 parses a respective utterance to produce corresponding syntactic relationship information. The word extractor 140 then utilizes the respective word extraction rules 125 to identify that the respective utterance includes the respective candidate word grouping 145 - 1 when_delivery.
- a respective intent value e.g., when_delivery
- FIG. 9 is an example diagram illustrating statistical information 160 indicating how often a respective intent value or candidate word grouping occurs in a pool of received utterances according to embodiments herein.
- a pool of received utterances 457 of the utterances in the received pool included the intent value (or group of words) speak_representative, 337 of the utterances in the received pool included the intent value (or group of words) cancel_delivery, 312 of the utterances in the received pool included the intent value (or group of words) place_order, etc.
- embodiments herein can include keeping track of a frequency of occurrence for each of the different intent values for a pool of received utterances.
- the speech processing system 100 is unsupervised and requires no training data.
- the speech processing system 100 can collect and record the statistical information 160 over time as the speech processing system 100 receives and processes the additional utterances. Accordingly, embodiments herein include maintaining statistical information for a pool of previously received utterances. As previously discussed, the statistical information 160 indicate a frequency of receiving utterances of different intent types. The speech processing system 100 updates the statistical information 160 to reflect that the different utterances were assigned the intent values.
- FIG. 10 is an example diagram illustrating intent values assigned to a respective subject matter label according to embodiments herein.
- the speech processing system 100 receives the utterance “I would like to speak with a representative to change my order and cancel a delivery” in response to a query such as “How may I assist you?”
- the syntactic parser 115 processes the received utterance to produce respective syntactic relationship information 120 for the utterance.
- the word extractor 140 applies the word extraction rules 125 (such as those in FIG. 4 ) and syntactic relationship information 120 to identify candidate word groupings 145 such as speak_representative, change_order, and cancel_delivery as possible intended meanings of the utterance.
- word extraction rules 125 such as those in FIG. 4
- syntactic relationship information 120 to identify candidate word groupings 145 such as speak_representative, change_order, and cancel_delivery as possible intended meanings of the utterance.
- the speech processing system 100 utilizes the identified syntactic relationships 120 of words to identify how the words in the received utterance are related.
- the speech processing system 100 then initiates application of word extraction rules or pattern rules to related words in the utterance to identify locations of words and produce candidate word groupings 145 .
- the word extraction rules 125 specifying which type of related words in the utterance to create the candidate sets of words.
- the syntactic parser 115 produces respective syntactic relationship information 120 for the received utterance “I would like to speak with a representative to change my order and cancel a delivery”.
- the word extractor 140 produces a first set of words (e.g., one of candidate word groupings 145 ) to include a first word and a second word in the utterance such as “speak” and “representative”, the first word “speak” is syntactically related to the second word “representative” as indicated by the identified syntactic relationships 120 as previously discussed;
- the word extractor 140 produces a second set of words (e.g., another of candidate word groupings 145 ) to include a third word such as “change” and a fourth word “order” in the utterance, the third word “change” is syntactically related to the fourth word “order” as indicated by syntactic relationship
- the word extractor 140 maps the combination of words including “speak” and “representative” to possible intent value speak_representative; the word extractor 140 maps the combination of words including “change” and “order” to possible intent value change_order; the word extractor 140 maps the combination of words including “cancel” and “delivery” to possible intent value cancel_delivery.
- the received utterance potentially can be classified in a number of different classes.
- the speech processing system 100 determines a dominant intent for assigning to the utterance based on statistical information 160 in FIG. 9 .
- the speech processing system 100 can determine how often the word groupings in the instance utterance under test appeared in other received utterances and select.
- the most often occurring intent value can be chosen for the instant utterance under test as the most likely intended meaning. For example, the intent value speak_representative appeared in 457 previously received utterances of a pool of received utterances, the intent value change_order appeared in 129 previously received utterances of the pool of received utterance, the intent value cancel_delivery appeared in 337 previously received utterances of a pool of received utterance.
- the analyzer 150 selects the candidate intent value speak_representative as being the most likely dominant intent for the utterance because it occurred most often in other previously received utterances. That is, in one example embodiment, the analyzer identifies a frequency of occurrence (e.g., number of utterances in a pool that include a specific grouping of words) that utterances in a pool of previously received utterances were of a same intent type as that of a first candidate intent value for the newly received utterance; the analyzer also identifies a frequency of occurrence that utterances in the pool of previously received utterances were of a same intent type as that of the second candidate intent value for the newly received utterance; and so on. The analyzer 150 then selects an intent value for the utterance under test “I would like to speak with a representative to change my order and cancel a delivery” based on the most often occurring intent value in previous utterances.
- a frequency of occurrence e.g., number of utterance
- the analyzer 150 can be configured to tag the respective utterance depending on the one or more intent values (namely, speak_representative, change_order, and cancel_delivery) identified for the utterance.
- FIG. 11 illustrates how to map a respective intent value for an utterance to a respective label.
- FIG. 11 is an example diagram illustrating intent values and corresponding assigned subject matter labels according to embodiments herein.
- the label map can include multiple labels for potentially tagging the received utterance.
- Each of the labels can be assigned multiple word groupings that commonly appear in utterance that fall into the corresponding label.
- the label “AGENT” is a candidate reserved for tagging any utterance including the word groupings speak-representative, speak_someone, talk_person, etc.;
- the label “SKIP_A_DELIVERY” can be reserved for tagging any utterance including the word groupings skip_delivery, skip_order, hold_delivery, etc.;
- the label “AGENT_BILLING” can be reserved for tagging any utterance including the word groupings have_bill, talk_bill, speak_billing, etc.; and so on.
- the analyzer 150 can utilize the label map 1100 to identify how to label received utterances.
- an utterance can be assigned one or more labels indicating a class in to which the utterance falls.
- the example utterance which produces candidate word groupings speak_representative, change_order, and cancel_delivery can be assigned labels AGENT, CHANGE_ITEMS, AND CANCEL_DELIVERY.
- the utterance also can be labeled with only a single label corresponding to the dominant intent value (e.g., speak_representative) such as AGENT.
- FIG. 12 is an example diagram illustrating possible association of one or more labels to an utterance according to embodiments herein.
- 457 utterances in a pool of previously received utterances included the intent value speak_representative.
- the appropriate label for each of the utterances was the label “AGENT”.
- the appropriate label for each of the utterances was the label “AGENT_BILLING”.
- the appropriate label for respective utterance was the label “BILLING”.
- the candidate word groupings 145 derived for an utterance can indicate that a respective utterance may fall under one or more of multiple different classes such as “AGENT” (a majority label for an utterance including speak_representative), “AGENT_BILLING” (a minority label for an utterance including speak_representative), and “BILLING” (a minority label for an utterance including speak_representative).
- FIG. 13 is a diagram illustrating an example computer architecture for executing a speech processing system 100 according to embodiments herein.
- Computer system 1300 can include one or more computerized devices such as a personal computer, workstation, portable computing device, console, network terminal, processing device, network device, etc., operating as a server, client, etc.
- the speech processing application 140 - 1 can be configured to include instructions to carry out any or all of the operations associated with syntactic parser 115 , word extractor 120 , analyzer 150 , etc.
- computer system 1300 of the present example includes an interconnect 1311 that couples computer readable storage media 1312 such as a non-transitory type of media in which digital information can be stored and retrieved, a processor 1313 , I/O interface 1314 , and a communications interface 1317 .
- I/O interface 1314 enables receipt of utterances 110 .
- I/O interface 1314 provides connectivity to repository 180 and, if present, other devices such as display screen, peripheral devices 316 , keyboard, computer mouse, etc.
- Resources such as word extraction rules 125 , statistical information 160 , syntactic relationship information 120 , candidate word groupings, etc. can be stored and retrieved from repository 180 .
- Computer readable storage medium 1312 can be any suitable device such as memory, optical storage, hard drive, floppy disk, etc. In one embodiment, the computer readable storage medium 1312 is a non-transitory storage media to store instructions and/or data.
- Communications interface 1317 enables the computer system 1300 and processor 1313 to communicate over a network 190 to retrieve information from remote sources and communicate with other computers.
- I/O interface 1314 enables processor 1313 to retrieve or attempt retrieval of stored information from repository 180 .
- computer readable storage media 1312 can be encoded with speech processing application 140 - 1 (e.g., software, firmware, etc.) executed by processor 1313 .
- speech processing application 140 - 1 e.g., software, firmware, etc.
- processor 1313 accesses computer readable storage media 1312 via the use of interconnect 1311 in order to launch, run, execute, interpret or otherwise perform the instructions of speech processing application 140 - 1 stored on computer readable storage medium 1312 .
- speech processing application 140 - 1 can include appropriate instructions, parsers, language models, analyzers, etc., to carry out any or all functionality associated with the speech processing system 100 as discussed herein.
- Execution of the speech processing application 140 - 1 produces processing functionality such as speech processing process 140 - 2 in processor 1313 .
- the speech processing process 140 - 2 associated with processor 1313 represents one or more aspects of executing speech processing application 140 - 1 within or upon the processor 1313 in the computer system 1300 .
- the computer system 1300 can include other processes and/or software and hardware components, such as an operating system that controls allocation and use of hardware resources to execute speech recognition application 140 - 1 .
- computer system may be any of various types of devices, including, but not limited to, a personal computer system, desktop computer, laptop, notebook, netbook computer, mainframe computer system, handheld computer, workstation, network computer, application server, storage device, a consumer electronics device such as a camera, camcorder, set top box, mobile device, video game console, handheld video game device, a peripheral device such as a switch, modem, router, or in general any type of computing or electronic device.
- a personal computer system desktop computer, laptop, notebook, netbook computer, mainframe computer system, handheld computer, workstation, network computer, application server, storage device, a consumer electronics device such as a camera, camcorder, set top box, mobile device, video game console, handheld video game device, a peripheral device such as a switch, modem, router, or in general any type of computing or electronic device.
- speech processing system 100 and speech processing application 140 - 1 Functionality supported by speech processing system 100 and speech processing application 140 - 1 will now be discussed via flowcharts in FIGS. 14-16 .
- the speech recognition system 140 can be configured to execute the steps in the flowcharts as discussed below.
- FIG. 14 is a flowchart 1400 illustrating a general technique of implementing a speech processing system 100 and related resources according to embodiments herein.
- step 1410 the speech processing system 100 parses an utterance 110 - 1 to identify syntactic relationships 120 - 1 amongst words in the utterance 110 - 1 .
- step 1420 the speech processing system 100 groups or creates sets of words from the utterance 110 - 1 based on word extraction rules and the syntactic relationships of words in the utterance 110 - 1 .
- step 1430 the speech processing system 100 maps each set of the sets of words (e.g., candidate word groupings 145 ) to a respective candidate intent value (e.g., possible intended meaning of the utterance).
- step 1440 the speech processing system 100 produces a list including candidate intent values for each of the sets of words (e.g., candidate word groupings 145 ).
- step 1450 the speech processing system 100 selects, from the list, a candidate intent value as being representative of an intent of the utterance.
- FIGS. 15 and 16 combine to form a flowchart 1500 (e.g., flowchart 1500 - 1 and flowchart 1500 - 2 ) illustrating implementation of a speech processing system 100 according to embodiments herein.
- a flowchart 1500 e.g., flowchart 1500 - 1 and flowchart 1500 - 2 .
- step 1510 the speech-processing system 100 parses text in a received utterance 110 - 1 to identify syntactic relationships amongst words in the utterance 110 - 1 .
- step 1515 the speech-processing system 100 groups or creates sets of words from the received utterance based on word extraction rules 125 and/or the syntactic relationships (as specified by syntactic relationship information 120 ) of words in the utterance.
- the speech-processing system 100 utilizes the identified syntactic relationships amongst words to identify groupings (e.g., candidate word groupings 145 ) of related words in the utterance.
- the speech-processing system 100 applies a set of word extraction rules 125 and/or patterns to the identified syntactic relationships and syntactic relationship information 120 to identify locations of words in the utterance to create the sets of words.
- step 1530 the speech-processing system 100 maps each set of the sets of words to a respective candidate intent value.
- the speech-processing system 100 produces a list including a candidate intent value for each of the sets of words.
- the list includes a first candidate intent value, a second candidate intent value, and so on.
- step 1610 the speech-processing system 100 selects, from the list, a candidate intent value as being representative of an intent of the received utterance.
- the speech-processing system 100 identifies a frequency of occurrence that utterances in a pool of previously received utterances were of a same intent type as that of a first candidate intent value.
- step 1620 the speech-processing system 100 identifies a frequency of occurrence that utterances in the pool of previously received utterances were of a same intent type as that of the second candidate intent value.
- the speech-processing system 100 selects the candidate intent value for the utterance depending on which of the first candidate intent value and the second candidate intent value occurred more soften in the pool for the previously received utterances.
- the selected candidate value indicates a dominant subject matter representative of the utterance.
- step 1630 the speech-processing system 100 identifies a tag representative of the selected candidate intent value for the utterance.
- step 1635 the speech-processing system 100 tags the utterance with the tag indicate a dominant subject matter intended by the utterance.
- An algorithm as described herein, and generally, is considered to be a self-consistent sequence of operations or similar processing leading to a desired result.
- operations or processing involve physical manipulation of physical quantities.
- quantities may take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared or otherwise manipulated. It has proven convenient at times, principally for reasons of common usage, to refer to such signals as bits, data, values, elements, symbols, characters, terms, numbers, numerals or the like. It should be understood, however, that all of these and similar terms are to be associated with appropriate physical quantities and are merely convenient labels.
Abstract
According to example configurations, a speech processing system can include a syntactic parser, a word extractor, word extraction rules, and an analyzer. The syntactic parser of the speech processing system parses the utterance to identify syntactic relationships amongst words in the utterance. The word extractor utilizes word extraction rules to identify groupings of related words in the utterance that most likely represent an intended meaning of the utterance. The analyzer in the speech processing system maps each set of the sets of words produced by the word extractor to a respective candidate intent value to produce a list of candidate intent values for the utterance. The analyzer is configured to select, from the list of candidate intent values (i.e., possible intended meanings) of the utterance, a particular candidate intent value as being representative of the intent (i.e., intended meaning) of the utterance.
Description
- Recent developments in computers and corresponding speech recognition software algorithms have made it possible to control computer equipment via spoken input. Thus, it is now becoming more common that users are able to control their computers, electronics, personal devices, call routing, etc., via speech input.
- Speech recognition systems are highly complex and operate by matching an acoustic signature of an utterance with acoustic signatures of words in a language model. As an example, according to conventional speech recognition systems, a microphone first converts a received acoustic signature of an uttered word into an electrical signal. An A/D (analog-to-digital) converter is typically used to convert the electrical signal into a digital representation of the uttered word. A digital signal processor converts the captured electrical signal from the time domain to the frequency domain.
- Generally, as another part of the speech recognition process, the digital signal processor breaks down the utterance into its spectral components. Typically, the amplitude or intensity of the digital signal at various frequencies and temporal locations are then compared to a language model to determine the one or more words that were uttered.
- Certain conventional speech recognition systems can be used for classifying utterances. For example, a conventional speech recognition system can receive and convert an utterance into respective text. In certain instances, a conventional speech recognition system can be configured to classify the utterance based on a key word in the utterance.
- Conventional speech recognition systems can suffer from drawbacks. For example, conventional speech recognition typically analyze an utterance for presence of words that indicate a likely class to which the utterance belongs. However, the accuracy of the classification can be quite low because an utterance may include many words, making the intended meaning of the utterance difficult to determine.
- Embodiments herein deviate with respect to conventional speech recognition systems to provide accurate speech utterance classification. For example, one embodiment herein includes a speech processing system to identify one or more intended meanings of a received utterance based on word groupings derived from words detected in the utterance. In one embodiment, the speech processing system tags the utterance with respective one or more tags indicative of one or more classes believed to represent an intended meaning of the utterance.
- More specifically, a speech processing system according to embodiments herein can include a syntactic parser, a word extractor, word pattern rules, and an analyzer. To identify an intended general meaning of the utterance, the speech processing system performs a number of tasks on a received utterance.
- In one embodiment, the syntactic parser of the speech processing system parses a received utterance to identify syntactic relationships amongst words in the utterance. The word extractor creates sets of words using words in the utterance based at least in part on the syntactic relationships identified by the parser. As an example, the word extractor can utilize the word pattern rules to identify groupings of related words in the utterance that most likely represent an intended meaning of the utterance.
- In one embodiment, the pattern rules specify which type and/or location of related words in the utterance that are to be used to create the sets of words. Accordingly, embodiments herein can include creating the sets of words can include utilizing the identified syntactic relationships of words to identify groupings of related words in the utterance; and applying a set of pattern rules or word extraction rules to the identified syntactic relationships to identify types and/or locations of words in the utterance to create the sets of words.
- Subsequent to creating the groupings of words, an analyzer in the speech processing system maps each set of the sets of words produced by the word extractor to a respective candidate intent value to produce a list of candidate intent values for the utterance. Thus, if there are multiple candidate word groupings derived form the utterance, the received utterance can be mapped multiple candidate intent values. As its name suggests, a candidate intent value is a possible intended meaning of the utterance.
- An utterance can have one or more intended meanings; the speech processing system can be configured to identify one or more most likely intended meanings of the utterance under test. In one embodiment, the analyzer is configured to select, from the list of possible intended meanings of the utterance, a particular candidate intent value as being representative of the intent (i.e., intended meaning) of the utterance.
- The speech processing system can be configured to maintain statistical information for a pool of previously received utterances to determine the meaning of future utterances. For example, as previously discussed, the statistical information can indicate a frequency of receiving utterances of different intent types. The statistical information can be updated over time to reflect that a most recently received and analyzed utterance was assigned the particular candidate intent value as discussed above.
- Selection of particular intent value amongst a group of possible intents for an utterance can be based on selection criteria employed by the analyzer. For example, in one embodiment, the analyzer identifies a frequency of occurrence that utterances in a pool of previously received utterances were of a same intent type as that of a first candidate intent value for the newly received utterance; the analyzer also identifies a frequency of occurrence that utterances in the pool of previously received utterances were of a same intent type as that of the second candidate intent value for the newly received utterance; and so on. As previously discussed, the analyzer can identify multiple possible classes in which to categorizes or classify the received utterance. In one embodiment, the analyzer then selects the a particular candidate intent value for assigning to the utterance depending on which of the possible candidate intent values (e.g., first candidate intent value, second candidate intent value, etc.) occurred more often in the pool for the previously received utterances. Thus, according to one embodiment, the analyzer can perform a frequency analysis and then sort the candidate meanings. As previously discussed, the selected candidate value indicates a likely dominant subject matter or theme of the utterance.
- In yet further embodiments, the speech processing system includes a set of tags or labels. In one embodiment, a tagging resource (such as the analyzer or other suitable resource) identifies an appropriate tag that is representative of the intent value (or selected meaning) selected for the utterance. The tagging resource then tags the utterance with the appropriate tag to indicate a likely dominant subject matter intended by words in the received utterance. Accordingly, embodiments herein can include classifying one or more received utterances using tags.
- As discussed above and below in further embodiments, techniques herein are well suited for use in software and/or hardware applications implementing speech recognition and classification of utterances based on intended meanings. However, it should be noted that embodiments herein are not limited to use in such applications and that the techniques discussed herein are well suited for other applications as well.
- These and other embodiments are discussed in more detail below.
- As mentioned above, note that embodiments herein can include a configuration of one or more computerized devices, workstations, handheld or laptop computers, or the like to carry out and/or support any or all of the method operations disclosed herein. In other words, one or more computerized devices or processors can be programmed and/or configured to operate as explained herein to carry out different embodiments of the invention.
- Yet other embodiments herein include software programs to perform the steps and operations summarized above and disclosed in detail below. One such embodiment comprises a computer program product including a non-transitory computer-readable storage medium on which software instructions are encoded for subsequent execution. The instructions, when executed in a computerized device having a processor, program and/or cause the processor to perform the operations disclosed herein. Such arrangements are typically provided as software, code, instructions, and/or other data (e.g., data structures) arranged or encoded on a non-transitory computer readable storage medium such as an optical medium (e.g., CD-ROM), floppy disk, hard disk, memory stick, etc., or other a medium such as firmware or microcode in one or more ROM, RAM, PROM, etc., or as an Application Specific Integrated Circuit (ASIC), etc. The software or firmware or other such configurations can be installed onto a computerized device to cause the computerized device to perform the techniques explained herein.
- Accordingly, one particular embodiment of the present disclosure is directed to a computer program product that includes a computer readable storage medium having instructions stored thereon for speech recognition such as converting of an utterance to corresponding text. For example, in one embodiment, the instructions, when executed by a processor of a respective computer device, cause the processor to: parse an utterance to identify syntactic relationships amongst words in the utterance; create or group sets of words from the utterance based at least in part on the syntactic relationships; map each set of the sets of words to a respective candidate intent value; produce a list of candidate intent values for the utterance based on the mapping; and select, from the list, a candidate intent value as being representative of an intent of the utterance.
- The ordering of the steps has been added for clarity sake. These steps can be performed in any suitable order.
- Other embodiments of the present disclosure include software programs and/or respective hardware to perform any of the method embodiment steps and operations summarized above and disclosed in detail below.
- It is to be understood that the system, method, apparatus, instructions on computer readable storage media, etc., as discussed herein can be embodied strictly as a software program, as a hybrid of software and hardware, or as hardware alone such as within a processor, or within an operating system or a within a software application. Example embodiments of the invention may be implemented within products and/or software applications such as those manufactured by Nuance Communications, Inc., Burlington, Mass., USA.
- Additionally, although each of the different features, techniques, configurations, etc., herein may be discussed in different places of this disclosure, it is intended that each of the concepts can be executed independently of each other or, where suitable, the concepts can be used in combination with each other. Accordingly, the one or more present inventions as described herein can be embodied and viewed in many different ways.
- Also, note that this preliminary discussion of embodiments herein does not specify every embodiment and/or incrementally novel aspect of the present disclosure or claimed invention(s). Instead, this brief description only presents general embodiments and corresponding points of novelty over conventional techniques. For additional details and/or possible perspectives (permutations) of the invention(s), and additional points of novelty, the reader is directed to the Detailed Description section and corresponding figures of the present disclosure as further discussed below.
-
FIG. 1 is an example block diagram of a speech processing system to perform intent mining for one or more utterances according to embodiments herein. -
FIG. 2 is an example diagram illustrating a sample utterance and corresponding relationship information generated by a syntactic parser according to embodiments herein. -
FIG. 3 is an example diagram illustrating a sample utterance and corresponding relationship information generated by a syntactic parser according to embodiments herein. -
FIG. 4 is an example diagram illustrating pattern or word extraction rules according to embodiments herein. -
FIG. 5 is an example diagram illustrating a listing of different utterances that have been classified under a representative intent value according to embodiments herein. -
FIG. 6 is an example diagram illustrating a listing of different utterances that have been classified under a representative intent value according to embodiments herein. -
FIG. 7 is an example diagram illustrating a listing of different utterances that have been classified under a representative intent value according to embodiments herein. -
FIG. 8 is an example diagram illustrating a listing of different utterances that have been classified under a representative intent value according to embodiments herein. -
FIG. 9 is an example diagram illustrating sets of words extracted from utterances and corresponding frequency information of occurrence according to embodiments herein. -
FIG. 10 is an example diagram illustrating application of intent mining to an example according to embodiments herein. -
FIG. 11 is an example diagram illustrating intent values (e.g., representative word groupings) assigned to a respective subject matter label according to embodiments herein. -
FIG. 12 is an example diagram illustrating intent values and corresponding assigned subject matter labels according to embodiments herein. -
FIG. 13 is a diagram illustrating an example computer architecture for executing a speech processing system according to embodiments herein. -
FIG. 14 is a flowchart illustrating an example method of implementing a speech processing system according to embodiments herein. -
FIGS. 15 and 16 combine to form a flowchart illustrating an example method of implementing speech processing system according to embodiments herein. - The foregoing and other objects, features, and advantages of the invention will be apparent from the following more particular description of preferred embodiments herein, as illustrated in the accompanying drawings in which like reference characters refer to the same parts throughout the different views. The drawings are not necessarily to scale, with emphasis instead being placed upon illustrating the embodiments, principles, concepts, etc.
- According to one embodiment, a speech processing system includes a syntactic parser, a word extractor, word extraction rules, and an analyzer. The syntactic parser of the speech processing system parses the utterance to identify syntactic relationships amongst words in the utterance. In accordance with the syntactic relationship information, the word extractor utilizes the word extraction rules to identify groupings of related words in the utterance that most likely represent an intended meaning of the utterance. The analyzer in the speech processing system maps each set of the sets of words produced by the word extractor to a respective candidate intent value to produce a list of candidate intent values for the utterance. The candidate intent values represent a possible intended meaning of the utterance. The analyzer is configured to select, from the list of candidate intent values (i.e., possible intended meanings) of the utterance, a particular candidate intent value as being representative of a dominant intent of the utterance.
- Now, referring to the figures,
FIG. 1 is an example diagram illustrating aspeech processing system 100 according to embodiments herein. As shown,speech processing system 100 includes asyntactic parser 115,word extractor 140, and ananalyzer 150. Thespeech processing system 100 can receive one or more utterances 110-1, 110-2, 100-3, . . . (collectively, utterances 110) in response to a query such as “How may I assist you?” - As previously discussed, embodiments herein deviate with respect to conventional speech recognition systems to provide accurate speech utterance classification. For example, one embodiment herein includes a
speech processing system 100 to identify one or more intended meanings of a receivedutterance 110. Thespeech processing system 100 can tag the utterance with respective one or more tags indicative of one or more classes that appear to best represent an intended meaning of theutterance 110. - To identify an intended general meaning of a received utterance or sequence of inputted text, the
speech processing system 110 performs a number of tasks on a received utterance. For example, thesyntactic parser 115 of thespeech processing system 100 converts the utterance to respective text and parses the text in theutterance 110 to producesyntactic relationship information 120. Thesyntactic relationship information 120 indicates syntactic relationships amongst the text-based words in theutterance 110. - The
word extractor 140 creates sets of words (e.g., candidate word groupings 145) using words in theutterance 110 based at least in part on thesyntactic relationship information 120 identified by thesyntactic parser 115. As an example, theword extractor 140 can utilize theword extraction rules 125 to identify one or more groupings of related words in the utterance that most likely represent an intended meaning of the utterances. - In one embodiment, the word extraction rules 125 includes patterns and/or templates that indicate, for example, which types of words, locations of words, etc., in the utterance will be used to create respective word groupings.
- In accordance with further embodiments, the
word extraction rules 125 specify which type and/or location of related words in the utterances are to be used to create the sets of words for a respective utterance. Accordingly, embodiments herein include creating the sets of words for a respective utterance under test can include utilizing the identifiedsyntactic relationship information 120 to identify groupings of related words in the respective utterance; and applying a set ofword extraction rules 125 to the identified syntactic relationships and utterance under test to identify types and/or locations of words in the utterance to create the sets of words. - Subsequent to creating one or more
candidate word groupings 145 for a respective utterance, ananalyzer 150 in thespeech processing system 150 maps each set of the sets of words (e.g., candidate word groupings 145) produced by theword extractor 140 to a respective candidate intent value to produce a list of candidate intent values for the utterance. As its name suggests, a candidate intent value is a possible intended meaning of the utterance. - Note that a candidate word grouping produced by the
speech processing system 100 for a respective utterance can include any number of words such as a single word or two or more words. Theword extractor 140 can produce one or multiplecandidate word groupings 145 for each utterance. - An utterance can have one or more intended meanings; the
speech processing system 100 can be configured to identify one or more most likely intended meanings of an utterance under test. If there is only one word grouping and corresponding candidate intent value produced for an utterance under test, then the analyzer can assign the single intended meaning of the single candidate intent value to the utterance. - If analysis of an utterance produces multiple possible candidates, subsequent to determining the list of possible intended meanings (e.g., candidate intent values) of an utterance under test, the
analyzer 150 applies further processing to narrow down the multiple possible intended meanings of the utterance to a at least most likely representative of the utterance. For example, in one embodiment, the analyzer is configured to select, from the list of possible intended meanings of the utterance as indicated by thecandidate word groupings 145, a particular candidate intent value as being representative of the intent (i.e., intended meaning) of the utterance. - This technique of processing can be applied to each of the received
utterances 110. Thus, in other words, thespeech processing system 100 can be configured to classify the received utterances based on intended meaning. - The
speech processing system 10 can be configured to maintainstatistical information 160 for a pool of previously received utterances. For example, as previously discussed, thestatistical information 160 can indicate an historical frequency of receiving utterances of different intent types. For newly received utterances and determination of respective intended meanings, thestatistical information 160 can be updated over time to reflect that a most recently received and analyzed utterance was assigned a particular candidate intent value. Thus, over time, thestatistical information 160 can be configured to keep track of how often thespeech processing system 100 receives utterances of each particular intent type. - Selection of an intent value amongst a group of possible intents to assign to a newly received utterance under test can be based on selection criteria employed by the
analyzer 150. In such an embodiment, theanalyzer 150 selects the a particular candidate intent value for assigning to the utterance depending on which of the possible candidate intent values (e.g., first candidate intent value, second candidate intent value, etc.) occurred more often in the pool for the previously received utterances as specified by thestatistical information 160. Thus, according to one embodiment, theanalyzer 150 can perform a frequency analysis and then sort the candidate meanings to perform intent mining. As previously discussed, the selected candidate value indicates a likely dominant subject matter or theme of the utterance. - One embodiment includes so-called clustering. In such an embodiment, for each intent, the
analyzer 150 compute such as respective frequency and sorts the intents. For (i=1—1, 1—2, . . . , i_n), let U={u —1,u —2, . . . , u_m} be the utterance containing i, For j in U, if the j is already not covered, then j is covered by i. Mark j covered. - Note that any suitable method can be implemented to perform clustering.
- In yet further embodiments, the
speech processing system 100 can include a set of tags for assigning to the utterances. In one embodiment, a tagging resource (such as theanalyzer 150 or other suitable resource) identifies an appropriate tag that is representative of the intent value (or selected meaning) for an utterance. The tagging resource then tags the utterance with the appropriate tag to indicate a likely dominant subject matter intended by words in the received utterance. Accordingly, embodiments herein can include classifying one or more received utterances and then applying tags to identify a respective intended meaning. -
FIG. 2 is an example diagram illustrating a sample utterance andcorresponding relationship information 120 according to embodiments herein.Syntactic parser 115 receives the example utterance 110-1 “I would like to speak with a customer representative.” Based on parsing, thesyntactic parser 115 produces syntactic relationship information 120-1 (p=pronoun, voltage=verb, a=adjective, network=noun, . . . ) as shown. By way of a non-limiting example, a Link Grammar parser can be used to parse the received utterance 110-1. -
FIG. 3 is an example diagram illustrating a sample utterance and corresponding relationship information according to embodiments herein.Syntactic parser 115 receives the example utterance “I would like to speak with a customer service representative.” Based on parsing rules, thesyntactic parser 115 produces syntactic relationship information 120-2 as shown (e.g., p=pronoun, voltage=verb, a=adjective, network=noun, . . . ). -
FIG. 4 is an example diagram illustratingword extraction rules 125 according to embodiments herein. In one embodiment, in conjunction with thesyntactic relationship information 125, the word extraction rules 125 are used to identify the location of different types of words in a received utterance that are to be used to generate respectivecandidate word groupings 145. - During operation, the
word extractor 140 applies these rules to producecandidate word groupings 145. In the above exampleFIG. 2 , theword extractor 140 applies word extraction rule 125-1 (FIG. 4 ) to example utterance 110-1 “I need to change my next order.” For this utterance 110-1 (FIG. 2 ) and using word extraction rule 125-1, Lword=“change” and Rword=“order” to produce the candidate word grouping change_order based on format Lword_Rword. - In the above example
FIG. 3 , theword extractor 140 applies word extraction rule 125-2 to example utterance 110-2 “I would like to speak with a customer representative.” For this utterance 110-2 and application of word extraction rule 125-2FIG. 4 ) as shown, MVp Lword=“speak”, MVp Xword=“with”, Js Xword=“with”, Js Rword=“representative” to produce the candidate word grouping speak_representative based on format Lword_Rword. - As discussed below, a given utterance can include one candidate word grouping or multiple candidate word groupings.
-
FIG. 5 is an example diagram illustrating a listing of different example utterances that have been classified under a respective intent value (e.g., speak_representative) according to embodiments herein. That is, processing of each of the example utterances inFIG. 5 via thespeech processing system 100 would produce the candidate word grouping 145-1 (e.g., a candidate intent value indicating a possible intended meaning of the utterance) for the respective utterance. For example, during processing,syntactic parser 115 parses a respective utterance to produce correspondingsyntactic relationship information 120. Theword extractor 140 then utilizes the respectiveword extraction rules 125 to identify that the respective utterance includes the respective candidate word grouping 145-1 speak_representative. -
FIG. 6 is an example diagram illustrating a listing of different utterances that have been classified under a respective intent value (e.g., cancel_delivery) according to embodiments herein. That is, processing of theexample utterances 110 inFIG. 6 via thespeech processing system 100 would produce the candidate word grouping 145-2 (e.g., a candidate intent value indicating a possible intended meaning of the utterance) for the respective utterance. For example, during processing,syntactic parser 115 would parse the utterances to produce corresponding syntactic relationship information. Theword extractor 140 then utilizes the respectiveword extraction rules 125 to identify that each of theutterances 110 as shown include the respective candidate word groupings 145-1. -
FIG. 7 is an example diagram illustrating a listing of different utterances that have been classified under a respective intent value (e.g., pay_bill) according to embodiments herein. That is, processing of each of theexample utterances 110 inFIG. 7 via thespeech processing system 100 would produce the candidate word grouping 145-3 (e.g., a candidate intent value indicating a possible intended meaning of the utterance) for the respective utterance. During processing,syntactic parser 115 parses a respective utterance to produce corresponding syntactic relationship information. Theword extractor 140 then utilizes the respectiveword extraction rules 125 to identify that the respective utterance includes the respective candidate word grouping 145-1 pay_bill. -
FIG. 8 is an example diagram illustrating a listing of different utterances that have been classified under a respective intent value (e.g., when_delivery) according to embodiments herein. That is, processing of each of theexample utterances 110 inFIG. 8 via thespeech processing system 100 would produce the candidate word grouping 145-4 (e.g., a candidate intent value indicating a possible intended meaning of the utterance) for the utterance. During processing,syntactic parser 115 parses a respective utterance to produce corresponding syntactic relationship information. Theword extractor 140 then utilizes the respectiveword extraction rules 125 to identify that the respective utterance includes the respective candidate word grouping 145-1 when_delivery. -
FIG. 9 is an example diagram illustratingstatistical information 160 indicating how often a respective intent value or candidate word grouping occurs in a pool of received utterances according to embodiments herein. For example, for a pool of received utterances, 457 of the utterances in the received pool included the intent value (or group of words) speak_representative, 337 of the utterances in the received pool included the intent value (or group of words) cancel_delivery, 312 of the utterances in the received pool included the intent value (or group of words) place_order, etc. Accordingly, embodiments herein can include keeping track of a frequency of occurrence for each of the different intent values for a pool of received utterances. - In one embodiment, the
speech processing system 100 is unsupervised and requires no training data. Thespeech processing system 100 can collect and record thestatistical information 160 over time as thespeech processing system 100 receives and processes the additional utterances. Accordingly, embodiments herein include maintaining statistical information for a pool of previously received utterances. As previously discussed, thestatistical information 160 indicate a frequency of receiving utterances of different intent types. Thespeech processing system 100 updates thestatistical information 160 to reflect that the different utterances were assigned the intent values. -
FIG. 10 is an example diagram illustrating intent values assigned to a respective subject matter label according to embodiments herein. Assume in this example that thespeech processing system 100 receives the utterance “I would like to speak with a representative to change my order and cancel a delivery” in response to a query such as “How may I assist you?” In such an instance, thesyntactic parser 115 processes the received utterance to produce respectivesyntactic relationship information 120 for the utterance. - In a manner as previously discussed, the
word extractor 140 applies the word extraction rules 125 (such as those inFIG. 4 ) andsyntactic relationship information 120 to identifycandidate word groupings 145 such as speak_representative, change_order, and cancel_delivery as possible intended meanings of the utterance. - As previously discussed, the
speech processing system 100 utilizes the identifiedsyntactic relationships 120 of words to identify how the words in the received utterance are related. Thespeech processing system 100 then initiates application of word extraction rules or pattern rules to related words in the utterance to identify locations of words and producecandidate word groupings 145. As previously discussed, theword extraction rules 125 specifying which type of related words in the utterance to create the candidate sets of words. - In the example shown, the
syntactic parser 115 produces respectivesyntactic relationship information 120 for the received utterance “I would like to speak with a representative to change my order and cancel a delivery”. By applyingword extraction rules 125 tosyntactic relationship information 120 and text in received utterance in a manner as previously discussed, theword extractor 140 produces a first set of words (e.g., one of candidate word groupings 145) to include a first word and a second word in the utterance such as “speak” and “representative”, the first word “speak” is syntactically related to the second word “representative” as indicated by the identifiedsyntactic relationships 120 as previously discussed; theword extractor 140 produces a second set of words (e.g., another of candidate word groupings 145) to include a third word such as “change” and a fourth word “order” in the utterance, the third word “change” is syntactically related to the fourth word “order” as indicated bysyntactic relationship information 120 for the received utterance; theword extractor 140 produces a third set of words (e.g., yet another of candidate word groupings 145) to include a fifth word such as “cancel” and a sixth word “delivery” in the utterance, the fifth word “cancel” is syntactically related to the sixth word “delivery” as indicated bysyntactic relationship information 120 for the utterance. - As shown, the
word extractor 140 maps the combination of words including “speak” and “representative” to possible intent value speak_representative; theword extractor 140 maps the combination of words including “change” and “order” to possible intent value change_order; theword extractor 140 maps the combination of words including “cancel” and “delivery” to possible intent value cancel_delivery. - Given these candidate word groupings 145 (namely, speak_representative, change_order, and cancel_delivery), the received utterance potentially can be classified in a number of different classes.
- In one embodiment, the
speech processing system 100 determines a dominant intent for assigning to the utterance based onstatistical information 160 inFIG. 9 . For example, thespeech processing system 100 can determine how often the word groupings in the instance utterance under test appeared in other received utterances and select. The most often occurring intent value can be chosen for the instant utterance under test as the most likely intended meaning. For example, the intent value speak_representative appeared in 457 previously received utterances of a pool of received utterances, the intent value change_order appeared in 129 previously received utterances of the pool of received utterance, the intent value cancel_delivery appeared in 337 previously received utterances of a pool of received utterance. - In this example, based on the analysis, the
analyzer 150 selects the candidate intent value speak_representative as being the most likely dominant intent for the utterance because it occurred most often in other previously received utterances. That is, in one example embodiment, the analyzer identifies a frequency of occurrence (e.g., number of utterances in a pool that include a specific grouping of words) that utterances in a pool of previously received utterances were of a same intent type as that of a first candidate intent value for the newly received utterance; the analyzer also identifies a frequency of occurrence that utterances in the pool of previously received utterances were of a same intent type as that of the second candidate intent value for the newly received utterance; and so on. Theanalyzer 150 then selects an intent value for the utterance under test “I would like to speak with a representative to change my order and cancel a delivery” based on the most often occurring intent value in previous utterances. - The
analyzer 150 can be configured to tag the respective utterance depending on the one or more intent values (namely, speak_representative, change_order, and cancel_delivery) identified for the utterance.FIG. 11 illustrates how to map a respective intent value for an utterance to a respective label. Based on -
FIG. 11 is an example diagram illustrating intent values and corresponding assigned subject matter labels according to embodiments herein. As shown, the label map can include multiple labels for potentially tagging the received utterance. Each of the labels can be assigned multiple word groupings that commonly appear in utterance that fall into the corresponding label. For example, the label “AGENT” is a candidate reserved for tagging any utterance including the word groupings speak-representative, speak_someone, talk_person, etc.; the label “SKIP_A_DELIVERY” can be reserved for tagging any utterance including the word groupings skip_delivery, skip_order, hold_delivery, etc.; the label “AGENT_BILLING” can be reserved for tagging any utterance including the word groupings have_bill, talk_bill, speak_billing, etc.; and so on. - Thus, the
analyzer 150 can utilize the label map 1100 to identify how to label received utterances. Depending on the embodiment, an utterance can be assigned one or more labels indicating a class in to which the utterance falls. If desired, the example utterance which produces candidate word groupings speak_representative, change_order, and cancel_delivery can be assigned labels AGENT, CHANGE_ITEMS, AND CANCEL_DELIVERY. The utterance also can be labeled with only a single label corresponding to the dominant intent value (e.g., speak_representative) such as AGENT. -
FIG. 12 is an example diagram illustrating possible association of one or more labels to an utterance according to embodiments herein. - For example, as previously discussed with respect to
FIGS. 5-9 , as shown inlisting - Accordingly, the
candidate word groupings 145 derived for an utterance can indicate that a respective utterance may fall under one or more of multiple different classes such as “AGENT” (a majority label for an utterance including speak_representative), “AGENT_BILLING” (a minority label for an utterance including speak_representative), and “BILLING” (a minority label for an utterance including speak_representative). -
FIG. 13 is a diagram illustrating an example computer architecture for executing aspeech processing system 100 according to embodiments herein. -
Computer system 1300 can include one or more computerized devices such as a personal computer, workstation, portable computing device, console, network terminal, processing device, network device, etc., operating as a server, client, etc. The speech processing application 140-1 can be configured to include instructions to carry out any or all of the operations associated withsyntactic parser 115,word extractor 120,analyzer 150, etc. - Note that the following discussion provides a basic embodiment indicating how to execute aspects of
speech processing system 100 according to embodiments herein. However, it should be noted that the actual configuration for carrying out the operations as described herein can vary depending on a respective application. - As shown,
computer system 1300 of the present example includes aninterconnect 1311 that couples computerreadable storage media 1312 such as a non-transitory type of media in which digital information can be stored and retrieved, aprocessor 1313, I/O interface 1314, and acommunications interface 1317. - I/
O interface 1314 enables receipt ofutterances 110. I/O interface 1314 provides connectivity torepository 180 and, if present, other devices such as display screen, peripheral devices 316, keyboard, computer mouse, etc. Resources such as word extraction rules 125,statistical information 160,syntactic relationship information 120, candidate word groupings, etc. can be stored and retrieved fromrepository 180. - Computer
readable storage medium 1312 can be any suitable device such as memory, optical storage, hard drive, floppy disk, etc. In one embodiment, the computerreadable storage medium 1312 is a non-transitory storage media to store instructions and/or data. -
Communications interface 1317 enables thecomputer system 1300 andprocessor 1313 to communicate over anetwork 190 to retrieve information from remote sources and communicate with other computers. I/O interface 1314 enablesprocessor 1313 to retrieve or attempt retrieval of stored information fromrepository 180. - As shown, computer
readable storage media 1312 can be encoded with speech processing application 140-1 (e.g., software, firmware, etc.) executed byprocessor 1313. - During operation of one embodiment,
processor 1313 accesses computerreadable storage media 1312 via the use ofinterconnect 1311 in order to launch, run, execute, interpret or otherwise perform the instructions of speech processing application 140-1 stored on computerreadable storage medium 1312. As previously discussed, speech processing application 140-1 can include appropriate instructions, parsers, language models, analyzers, etc., to carry out any or all functionality associated with thespeech processing system 100 as discussed herein. - Execution of the speech processing application 140-1 produces processing functionality such as speech processing process 140-2 in
processor 1313. In other words, the speech processing process 140-2 associated withprocessor 1313 represents one or more aspects of executing speech processing application 140-1 within or upon theprocessor 1313 in thecomputer system 1300. - Those skilled in the art will understand that the
computer system 1300 can include other processes and/or software and hardware components, such as an operating system that controls allocation and use of hardware resources to execute speech recognition application 140-1. - In accordance with different embodiments, note that computer system may be any of various types of devices, including, but not limited to, a personal computer system, desktop computer, laptop, notebook, netbook computer, mainframe computer system, handheld computer, workstation, network computer, application server, storage device, a consumer electronics device such as a camera, camcorder, set top box, mobile device, video game console, handheld video game device, a peripheral device such as a switch, modem, router, or in general any type of computing or electronic device.
- Functionality supported by
speech processing system 100 and speech processing application 140-1 will now be discussed via flowcharts inFIGS. 14-16 . As discussed above, thespeech recognition system 140 can be configured to execute the steps in the flowcharts as discussed below. - Note that there will be some overlap with respect to concepts discussed above. Also, note that the steps in the below flowcharts need not always be executed in the order shown. That is, the steps can be executed in any suitable order.
-
FIG. 14 is aflowchart 1400 illustrating a general technique of implementing aspeech processing system 100 and related resources according to embodiments herein. - In
step 1410, thespeech processing system 100 parses an utterance 110-1 to identify syntactic relationships 120-1 amongst words in the utterance 110-1. - In
step 1420, thespeech processing system 100 groups or creates sets of words from the utterance 110-1 based on word extraction rules and the syntactic relationships of words in the utterance 110-1. - In
step 1430, thespeech processing system 100 maps each set of the sets of words (e.g., candidate word groupings 145) to a respective candidate intent value (e.g., possible intended meaning of the utterance). - In
step 1440, thespeech processing system 100 produces a list including candidate intent values for each of the sets of words (e.g., candidate word groupings 145). - In
step 1450, thespeech processing system 100 selects, from the list, a candidate intent value as being representative of an intent of the utterance. -
FIGS. 15 and 16 combine to form a flowchart 1500 (e.g., flowchart 1500-1 and flowchart 1500-2) illustrating implementation of aspeech processing system 100 according to embodiments herein. - In
step 1510, the speech-processing system 100 parses text in a received utterance 110-1 to identify syntactic relationships amongst words in the utterance 110-1. - In
step 1515, the speech-processing system 100 groups or creates sets of words from the received utterance based on word extraction rules 125 and/or the syntactic relationships (as specified by syntactic relationship information 120) of words in the utterance. - In sub-step 1520, the speech-
processing system 100 utilizes the identified syntactic relationships amongst words to identify groupings (e.g., candidate word groupings 145) of related words in the utterance. - In sub-step 1525, the speech-
processing system 100 applies a set of word extraction rules 125 and/or patterns to the identified syntactic relationships andsyntactic relationship information 120 to identify locations of words in the utterance to create the sets of words. - In
step 1530, the speech-processing system 100 maps each set of the sets of words to a respective candidate intent value. - In
step 1535, the speech-processing system 100 produces a list including a candidate intent value for each of the sets of words. In one embodiment, the list includes a first candidate intent value, a second candidate intent value, and so on. - In step 1610, the speech-
processing system 100 selects, from the list, a candidate intent value as being representative of an intent of the received utterance. - In sub-step 1615, the speech-
processing system 100 identifies a frequency of occurrence that utterances in a pool of previously received utterances were of a same intent type as that of a first candidate intent value. - In
step 1620, the speech-processing system 100 identifies a frequency of occurrence that utterances in the pool of previously received utterances were of a same intent type as that of the second candidate intent value. - In
step 1625, the speech-processing system 100 selects the candidate intent value for the utterance depending on which of the first candidate intent value and the second candidate intent value occurred more soften in the pool for the previously received utterances. The selected candidate value indicates a dominant subject matter representative of the utterance. - In step 1630, the speech-
processing system 100 identifies a tag representative of the selected candidate intent value for the utterance. - In
step 1635, the speech-processing system 100 tags the utterance with the tag indicate a dominant subject matter intended by the utterance. - As discussed above, techniques herein are well suited for use in software and/or hardware applications implementing speech recognition and classification of utterances based on intended meanings. However, it should be noted that embodiments herein are not limited to use in such applications and that the techniques discussed herein are well suited for other applications
- Based on the description set forth herein, numerous specific details have been set forth to provide a thorough understanding of claimed subject matter. However, it will be understood by those skilled in the art that claimed subject matter may be practiced without these specific details. In other instances, methods, apparatuses, systems, etc., that would be known by one of ordinary skill have not been described in detail so as not to obscure claimed subject matter. Some portions of the detailed description have been presented in terms of algorithms or symbolic representations of operations on data bits or binary digital signals stored within a computing system memory, such as a computer memory. These algorithmic descriptions or representations are examples of techniques used by those of ordinary skill in the data processing arts to convey the substance of their work to others skilled in the art. An algorithm as described herein, and generally, is considered to be a self-consistent sequence of operations or similar processing leading to a desired result. In this context, operations or processing involve physical manipulation of physical quantities. Typically, although not necessarily, such quantities may take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared or otherwise manipulated. It has proven convenient at times, principally for reasons of common usage, to refer to such signals as bits, data, values, elements, symbols, characters, terms, numbers, numerals or the like. It should be understood, however, that all of these and similar terms are to be associated with appropriate physical quantities and are merely convenient labels. Unless specifically stated otherwise, as apparent from the following discussion, it is appreciated that throughout this specification discussions utilizing terms such as “processing,” “computing,” “calculating,” “determining” or the like refer to actions or processes of a computing platform, such as a computer or a similar electronic computing device, that manipulates or transforms data represented as physical electronic or magnetic quantities within memories, registers, or other information storage devices, transmission devices, or display devices of the computing platform.
- While this invention has been particularly shown and described with references to preferred embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the present application as defined b the appended claims. Such variations are intended to be covered by the scope of this present application. As such, the foregoing description of embodiments of the present application is not intended to be limiting. Rather, any limitations to the invention are presented in the following claims.
Claims (2)
1. A method comprising:
parsing an utterance to identify syntactic relationships amongst words in the utterance;
creating sets of words from the utterance based on the syntactic relationships;
mapping each set of the sets of words to a respective candidate intent value to produce a list of candidate intent values for the utterance; and
selecting, from the list, a candidate intent value as being representative of an intent of the utterance.
2-23. (canceled)
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US14/184,379 US20140180692A1 (en) | 2011-02-28 | 2014-02-19 | Intent mining via analysis of utterances |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US13/037,114 US8688453B1 (en) | 2011-02-28 | 2011-02-28 | Intent mining via analysis of utterances |
US14/184,379 US20140180692A1 (en) | 2011-02-28 | 2014-02-19 | Intent mining via analysis of utterances |
Related Parent Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US13/037,114 Continuation US8688453B1 (en) | 2011-02-28 | 2011-02-28 | Intent mining via analysis of utterances |
Publications (1)
Publication Number | Publication Date |
---|---|
US20140180692A1 true US20140180692A1 (en) | 2014-06-26 |
Family
ID=50348938
Family Applications (2)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US13/037,114 Active 2031-09-21 US8688453B1 (en) | 2011-02-28 | 2011-02-28 | Intent mining via analysis of utterances |
US14/184,379 Abandoned US20140180692A1 (en) | 2011-02-28 | 2014-02-19 | Intent mining via analysis of utterances |
Family Applications Before (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US13/037,114 Active 2031-09-21 US8688453B1 (en) | 2011-02-28 | 2011-02-28 | Intent mining via analysis of utterances |
Country Status (1)
Country | Link |
---|---|
US (2) | US8688453B1 (en) |
Cited By (11)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US8983840B2 (en) * | 2012-06-19 | 2015-03-17 | International Business Machines Corporation | Intent discovery in audio or text-based conversation |
US20180203833A1 (en) * | 2016-11-04 | 2018-07-19 | Semantic Machines, Inc. | Data collection for a new conversational dialogue system |
US10586530B2 (en) | 2017-02-23 | 2020-03-10 | Semantic Machines, Inc. | Expandable dialogue system |
US20200142719A1 (en) * | 2018-11-02 | 2020-05-07 | International Business Machines Corporation | Automatic generation of chatbot meta communication |
US10713288B2 (en) | 2017-02-08 | 2020-07-14 | Semantic Machines, Inc. | Natural language content generator |
US10762892B2 (en) | 2017-02-23 | 2020-09-01 | Semantic Machines, Inc. | Rapid deployment of dialogue system |
US11069340B2 (en) | 2017-02-23 | 2021-07-20 | Microsoft Technology Licensing, Llc | Flexible and expandable dialogue system |
US11132499B2 (en) | 2017-08-28 | 2021-09-28 | Microsoft Technology Licensing, Llc | Robust expandable dialogue system |
CN113657120A (en) * | 2021-08-23 | 2021-11-16 | 深圳科卫机器人科技有限公司 | Human-computer interaction intention analysis method and device, computer equipment and storage medium |
US11182565B2 (en) * | 2018-02-23 | 2021-11-23 | Samsung Electronics Co., Ltd. | Method to learn personalized intents |
US11314940B2 (en) | 2018-05-22 | 2022-04-26 | Samsung Electronics Co., Ltd. | Cross domain personalized vocabulary learning in intelligent assistants |
Families Citing this family (159)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US8677377B2 (en) | 2005-09-08 | 2014-03-18 | Apple Inc. | Method and apparatus for building an intelligent automated assistant |
US9318108B2 (en) | 2010-01-18 | 2016-04-19 | Apple Inc. | Intelligent automated assistant |
US8977255B2 (en) | 2007-04-03 | 2015-03-10 | Apple Inc. | Method and system for operating a multi-function portable electronic device using voice-activation |
US10002189B2 (en) | 2007-12-20 | 2018-06-19 | Apple Inc. | Method and apparatus for searching using an active ontology |
US9330720B2 (en) | 2008-01-03 | 2016-05-03 | Apple Inc. | Methods and apparatus for altering audio output signals |
US8996376B2 (en) | 2008-04-05 | 2015-03-31 | Apple Inc. | Intelligent text-to-speech conversion |
US20100030549A1 (en) | 2008-07-31 | 2010-02-04 | Lee Michael M | Mobile device having human language translation capability with positional feedback |
US8676904B2 (en) | 2008-10-02 | 2014-03-18 | Apple Inc. | Electronic devices with voice command and contextual data processing capabilities |
US20120311585A1 (en) | 2011-06-03 | 2012-12-06 | Apple Inc. | Organizing task items that represent tasks to perform |
US10241752B2 (en) | 2011-09-30 | 2019-03-26 | Apple Inc. | Interface for a virtual digital assistant |
US10241644B2 (en) | 2011-06-03 | 2019-03-26 | Apple Inc. | Actionable reminder entries |
US9431006B2 (en) | 2009-07-02 | 2016-08-30 | Apple Inc. | Methods and apparatuses for automatic speech recognition |
US10276170B2 (en) | 2010-01-18 | 2019-04-30 | Apple Inc. | Intelligent automated assistant |
US8682667B2 (en) | 2010-02-25 | 2014-03-25 | Apple Inc. | User profiling for selecting user specific voice input processing information |
US9262612B2 (en) | 2011-03-21 | 2016-02-16 | Apple Inc. | Device access using voice authentication |
US10057736B2 (en) | 2011-06-03 | 2018-08-21 | Apple Inc. | Active transport based notifications |
US10134385B2 (en) | 2012-03-02 | 2018-11-20 | Apple Inc. | Systems and methods for name pronunciation |
US10417037B2 (en) | 2012-05-15 | 2019-09-17 | Apple Inc. | Systems and methods for integrating third party services with a digital assistant |
US9721563B2 (en) | 2012-06-08 | 2017-08-01 | Apple Inc. | Name recognition system |
US9424840B1 (en) * | 2012-08-31 | 2016-08-23 | Amazon Technologies, Inc. | Speech recognition platforms |
US9547647B2 (en) | 2012-09-19 | 2017-01-17 | Apple Inc. | Voice-based media searching |
KR20230137475A (en) | 2013-02-07 | 2023-10-04 | 애플 인크. | Voice trigger for a digital assistant |
US10652394B2 (en) | 2013-03-14 | 2020-05-12 | Apple Inc. | System and method for processing voicemail |
US10748529B1 (en) | 2013-03-15 | 2020-08-18 | Apple Inc. | Voice activated device for use with a voice-based digital assistant |
WO2014197334A2 (en) | 2013-06-07 | 2014-12-11 | Apple Inc. | System and method for user-specified pronunciation of words for speech synthesis and recognition |
WO2014197335A1 (en) | 2013-06-08 | 2014-12-11 | Apple Inc. | Interpreting and acting upon commands that involve sharing information with remote devices |
US10176167B2 (en) | 2013-06-09 | 2019-01-08 | Apple Inc. | System and method for inferring user intent from speech inputs |
EP3937002A1 (en) | 2013-06-09 | 2022-01-12 | Apple Inc. | Device, method, and graphical user interface for enabling conversation persistence across two or more instances of a digital assistant |
US10296160B2 (en) | 2013-12-06 | 2019-05-21 | Apple Inc. | Method for extracting salient dialog usage from live data |
US9430463B2 (en) | 2014-05-30 | 2016-08-30 | Apple Inc. | Exemplar-based natural language processing |
EP3480811A1 (en) | 2014-05-30 | 2019-05-08 | Apple Inc. | Multi-command single utterance input method |
US9715875B2 (en) | 2014-05-30 | 2017-07-25 | Apple Inc. | Reducing the need for manual start/end-pointing and trigger phrases |
US9633004B2 (en) | 2014-05-30 | 2017-04-25 | Apple Inc. | Better resolution when referencing to concepts |
US10170123B2 (en) | 2014-05-30 | 2019-01-01 | Apple Inc. | Intelligent assistant for home automation |
US9338493B2 (en) | 2014-06-30 | 2016-05-10 | Apple Inc. | Intelligent automated assistant for TV user interactions |
US9818400B2 (en) | 2014-09-11 | 2017-11-14 | Apple Inc. | Method and apparatus for discovering trending terms in speech requests |
US10127911B2 (en) | 2014-09-30 | 2018-11-13 | Apple Inc. | Speaker identification and unsupervised speaker adaptation techniques |
US9668121B2 (en) | 2014-09-30 | 2017-05-30 | Apple Inc. | Social reminders |
US10074360B2 (en) | 2014-09-30 | 2018-09-11 | Apple Inc. | Providing an indication of the suitability of speech recognition |
US9842593B2 (en) | 2014-11-14 | 2017-12-12 | At&T Intellectual Property I, L.P. | Multi-level content analysis and response |
US10152299B2 (en) | 2015-03-06 | 2018-12-11 | Apple Inc. | Reducing response latency of intelligent automated assistants |
US10567477B2 (en) | 2015-03-08 | 2020-02-18 | Apple Inc. | Virtual assistant continuity |
US9721566B2 (en) | 2015-03-08 | 2017-08-01 | Apple Inc. | Competing devices responding to voice triggers |
US9886953B2 (en) | 2015-03-08 | 2018-02-06 | Apple Inc. | Virtual assistant activation |
WO2016151692A1 (en) * | 2015-03-20 | 2016-09-29 | 株式会社 東芝 | Tagging support device, method and program |
US10460227B2 (en) | 2015-05-15 | 2019-10-29 | Apple Inc. | Virtual assistant in a communication session |
US10200824B2 (en) | 2015-05-27 | 2019-02-05 | Apple Inc. | Systems and methods for proactively identifying and surfacing relevant content on a touch-sensitive device |
US10083688B2 (en) | 2015-05-27 | 2018-09-25 | Apple Inc. | Device voice control for selecting a displayed affordance |
US9578173B2 (en) | 2015-06-05 | 2017-02-21 | Apple Inc. | Virtual assistant aided communication with 3rd party service in a communication session |
US11025565B2 (en) | 2015-06-07 | 2021-06-01 | Apple Inc. | Personalized prediction of responses for instant messaging |
US20160378747A1 (en) | 2015-06-29 | 2016-12-29 | Apple Inc. | Virtual assistant for media playback |
US10387569B2 (en) * | 2015-08-28 | 2019-08-20 | Freedom Solutions Group, Llc | Automated document analysis comprising a user interface based on content types |
US10747498B2 (en) | 2015-09-08 | 2020-08-18 | Apple Inc. | Zero latency digital assistant |
US10331312B2 (en) | 2015-09-08 | 2019-06-25 | Apple Inc. | Intelligent automated assistant in a media environment |
US10740384B2 (en) | 2015-09-08 | 2020-08-11 | Apple Inc. | Intelligent automated assistant for media search and playback |
US10671428B2 (en) | 2015-09-08 | 2020-06-02 | Apple Inc. | Distributed personal assistant |
US10366158B2 (en) | 2015-09-29 | 2019-07-30 | Apple Inc. | Efficient word encoding for recurrent neural network language models |
US11010550B2 (en) | 2015-09-29 | 2021-05-18 | Apple Inc. | Unified language modeling framework for word prediction, auto-completion and auto-correction |
US10691473B2 (en) | 2015-11-06 | 2020-06-23 | Apple Inc. | Intelligent automated assistant in a messaging environment |
US10956666B2 (en) | 2015-11-09 | 2021-03-23 | Apple Inc. | Unconventional virtual assistant interactions |
US10049668B2 (en) | 2015-12-02 | 2018-08-14 | Apple Inc. | Applying neural network language models to weighted finite state transducers for automatic speech recognition |
US10223066B2 (en) | 2015-12-23 | 2019-03-05 | Apple Inc. | Proactive assistance based on dialog communication between devices |
US10446143B2 (en) | 2016-03-14 | 2019-10-15 | Apple Inc. | Identification of voice inputs providing credentials |
US9934775B2 (en) | 2016-05-26 | 2018-04-03 | Apple Inc. | Unit-selection text-to-speech synthesis based on predicted concatenation parameters |
US9972304B2 (en) | 2016-06-03 | 2018-05-15 | Apple Inc. | Privacy preserving distributed evaluation framework for embedded personalized systems |
US10249300B2 (en) | 2016-06-06 | 2019-04-02 | Apple Inc. | Intelligent list reading |
US10049670B2 (en) * | 2016-06-06 | 2018-08-14 | Google Llc | Providing voice action discoverability example for trigger term |
US11227589B2 (en) | 2016-06-06 | 2022-01-18 | Apple Inc. | Intelligent list reading |
US10049663B2 (en) | 2016-06-08 | 2018-08-14 | Apple, Inc. | Intelligent automated assistant for media exploration |
DK179309B1 (en) | 2016-06-09 | 2018-04-23 | Apple Inc | Intelligent automated assistant in a home environment |
US10192552B2 (en) | 2016-06-10 | 2019-01-29 | Apple Inc. | Digital assistant providing whispered speech |
US10490187B2 (en) | 2016-06-10 | 2019-11-26 | Apple Inc. | Digital assistant providing automated status report |
US10067938B2 (en) | 2016-06-10 | 2018-09-04 | Apple Inc. | Multilingual word prediction |
US10586535B2 (en) | 2016-06-10 | 2020-03-10 | Apple Inc. | Intelligent digital assistant in a multi-tasking environment |
US10509862B2 (en) | 2016-06-10 | 2019-12-17 | Apple Inc. | Dynamic phrase expansion of language input |
DK201670540A1 (en) | 2016-06-11 | 2018-01-08 | Apple Inc | Application integration with a digital assistant |
DK179049B1 (en) | 2016-06-11 | 2017-09-18 | Apple Inc | Data driven natural language event detection and classification |
DK179343B1 (en) | 2016-06-11 | 2018-05-14 | Apple Inc | Intelligent task discovery |
DK179415B1 (en) | 2016-06-11 | 2018-06-14 | Apple Inc | Intelligent device arbitration and control |
US10474753B2 (en) | 2016-09-07 | 2019-11-12 | Apple Inc. | Language identification using recurrent neural networks |
US10043516B2 (en) | 2016-09-23 | 2018-08-07 | Apple Inc. | Intelligent automated assistant |
US11281993B2 (en) | 2016-12-05 | 2022-03-22 | Apple Inc. | Model and ensemble compression for metric learning |
US10593346B2 (en) | 2016-12-22 | 2020-03-17 | Apple Inc. | Rank-reduced token representation for automatic speech recognition |
US11204787B2 (en) | 2017-01-09 | 2021-12-21 | Apple Inc. | Application integration with a digital assistant |
US9961200B1 (en) * | 2017-03-28 | 2018-05-01 | Bank Of America Corporation | Derived intent collision detection for use in a multi-intent matrix |
US20180288230A1 (en) * | 2017-03-29 | 2018-10-04 | International Business Machines Corporation | Intention detection and handling of incoming calls |
DK201770383A1 (en) | 2017-05-09 | 2018-12-14 | Apple Inc. | User interface for correcting recognition errors |
US10417266B2 (en) | 2017-05-09 | 2019-09-17 | Apple Inc. | Context-aware ranking of intelligent response suggestions |
DK180048B1 (en) | 2017-05-11 | 2020-02-04 | Apple Inc. | MAINTAINING THE DATA PROTECTION OF PERSONAL INFORMATION |
DK201770439A1 (en) | 2017-05-11 | 2018-12-13 | Apple Inc. | Offline personal assistant |
US10395654B2 (en) | 2017-05-11 | 2019-08-27 | Apple Inc. | Text normalization based on a data-driven learning network |
US10726832B2 (en) | 2017-05-11 | 2020-07-28 | Apple Inc. | Maintaining privacy of personal information |
DK201770428A1 (en) | 2017-05-12 | 2019-02-18 | Apple Inc. | Low-latency intelligent automated assistant |
DK179496B1 (en) | 2017-05-12 | 2019-01-15 | Apple Inc. | USER-SPECIFIC Acoustic Models |
US11301477B2 (en) | 2017-05-12 | 2022-04-12 | Apple Inc. | Feedback analysis of a digital assistant |
DK179745B1 (en) | 2017-05-12 | 2019-05-01 | Apple Inc. | SYNCHRONIZATION AND TASK DELEGATION OF A DIGITAL ASSISTANT |
DK201770432A1 (en) | 2017-05-15 | 2018-12-21 | Apple Inc. | Hierarchical belief states for digital assistants |
DK201770431A1 (en) | 2017-05-15 | 2018-12-20 | Apple Inc. | Optimizing dialogue policy decisions for digital assistants using implicit feedback |
US10311144B2 (en) | 2017-05-16 | 2019-06-04 | Apple Inc. | Emoji word sense disambiguation |
US10403278B2 (en) | 2017-05-16 | 2019-09-03 | Apple Inc. | Methods and systems for phonetic matching in digital assistant services |
DK179560B1 (en) | 2017-05-16 | 2019-02-18 | Apple Inc. | Far-field extension for digital assistant services |
US20180336275A1 (en) | 2017-05-16 | 2018-11-22 | Apple Inc. | Intelligent automated assistant for media exploration |
US20180336892A1 (en) | 2017-05-16 | 2018-11-22 | Apple Inc. | Detecting a trigger of a digital assistant |
US10657328B2 (en) | 2017-06-02 | 2020-05-19 | Apple Inc. | Multi-task recurrent neural network architecture for efficient morphology handling in neural language modeling |
KR102509821B1 (en) | 2017-09-18 | 2023-03-14 | 삼성전자주식회사 | Method and apparatus for generating oos(out-of-service) sentence |
US10445429B2 (en) | 2017-09-21 | 2019-10-15 | Apple Inc. | Natural language understanding using vocabularies with compressed serialized tries |
US10755051B2 (en) | 2017-09-29 | 2020-08-25 | Apple Inc. | Rule-based natural language processing |
US10636424B2 (en) | 2017-11-30 | 2020-04-28 | Apple Inc. | Multi-turn canned dialog |
US10733982B2 (en) | 2018-01-08 | 2020-08-04 | Apple Inc. | Multi-directional dialog |
US10733375B2 (en) | 2018-01-31 | 2020-08-04 | Apple Inc. | Knowledge-based framework for improving natural language understanding |
US10789959B2 (en) | 2018-03-02 | 2020-09-29 | Apple Inc. | Training speaker recognition models for digital assistants |
US10592604B2 (en) | 2018-03-12 | 2020-03-17 | Apple Inc. | Inverse text normalization for automatic speech recognition |
US10684950B2 (en) | 2018-03-15 | 2020-06-16 | Bank Of America Corporation | System for triggering cross channel data caching |
US10497366B2 (en) * | 2018-03-23 | 2019-12-03 | Servicenow, Inc. | Hybrid learning system for natural language understanding |
US10818288B2 (en) | 2018-03-26 | 2020-10-27 | Apple Inc. | Natural assistant interaction |
US10909331B2 (en) | 2018-03-30 | 2021-02-02 | Apple Inc. | Implicit identification of translation payload with neural machine translation |
US11145294B2 (en) | 2018-05-07 | 2021-10-12 | Apple Inc. | Intelligent automated assistant for delivering content from user experiences |
US10928918B2 (en) | 2018-05-07 | 2021-02-23 | Apple Inc. | Raise to speak |
US10984780B2 (en) | 2018-05-21 | 2021-04-20 | Apple Inc. | Global semantic word embeddings using bi-directional recurrent neural networks |
US10665228B2 (en) | 2018-05-23 | 2020-05-26 | Bank of America Corporaiton | Quantum technology for use with extracting intents from linguistics |
DK180639B1 (en) | 2018-06-01 | 2021-11-04 | Apple Inc | DISABILITY OF ATTENTION-ATTENTIVE VIRTUAL ASSISTANT |
US11386266B2 (en) | 2018-06-01 | 2022-07-12 | Apple Inc. | Text correction |
DK201870355A1 (en) | 2018-06-01 | 2019-12-16 | Apple Inc. | Virtual assistant operation in multi-device environments |
DK179822B1 (en) | 2018-06-01 | 2019-07-12 | Apple Inc. | Voice interaction at a primary device to access call functionality of a companion device |
US10892996B2 (en) | 2018-06-01 | 2021-01-12 | Apple Inc. | Variable latency device coordination |
US11076039B2 (en) | 2018-06-03 | 2021-07-27 | Apple Inc. | Accelerated task performance |
US10685645B2 (en) * | 2018-08-09 | 2020-06-16 | Bank Of America Corporation | Identification of candidate training utterances from human conversations with an intelligent interactive assistant |
US11010561B2 (en) | 2018-09-27 | 2021-05-18 | Apple Inc. | Sentiment prediction from textual data |
US11462215B2 (en) | 2018-09-28 | 2022-10-04 | Apple Inc. | Multi-modal inputs for voice commands |
US10839159B2 (en) | 2018-09-28 | 2020-11-17 | Apple Inc. | Named entity normalization in a spoken dialog system |
US11170166B2 (en) | 2018-09-28 | 2021-11-09 | Apple Inc. | Neural typographical error modeling via generative adversarial networks |
US11475898B2 (en) | 2018-10-26 | 2022-10-18 | Apple Inc. | Low-latency multi-speaker speech recognition |
CN109657229A (en) * | 2018-10-31 | 2019-04-19 | 北京奇艺世纪科技有限公司 | A kind of intention assessment model generating method, intension recognizing method and device |
US11638059B2 (en) | 2019-01-04 | 2023-04-25 | Apple Inc. | Content playback on multiple devices |
US11348573B2 (en) | 2019-03-18 | 2022-05-31 | Apple Inc. | Multimodality in digital assistant systems |
US11715467B2 (en) * | 2019-04-17 | 2023-08-01 | Tempus Labs, Inc. | Collaborative artificial intelligence method and system |
US20200342874A1 (en) * | 2019-04-26 | 2020-10-29 | Oracle International Corporation | Handling explicit invocation of chatbots |
US11307752B2 (en) | 2019-05-06 | 2022-04-19 | Apple Inc. | User configurable task triggers |
US11217251B2 (en) | 2019-05-06 | 2022-01-04 | Apple Inc. | Spoken notifications |
US11475884B2 (en) | 2019-05-06 | 2022-10-18 | Apple Inc. | Reducing digital assistant latency when a language is incorrectly determined |
US11423908B2 (en) | 2019-05-06 | 2022-08-23 | Apple Inc. | Interpreting spoken requests |
US11140099B2 (en) | 2019-05-21 | 2021-10-05 | Apple Inc. | Providing message response suggestions |
DK201970510A1 (en) | 2019-05-31 | 2021-02-11 | Apple Inc | Voice identification in digital assistant systems |
US11496600B2 (en) | 2019-05-31 | 2022-11-08 | Apple Inc. | Remote execution of machine-learned models |
US11289073B2 (en) | 2019-05-31 | 2022-03-29 | Apple Inc. | Device text to speech |
DK180129B1 (en) | 2019-05-31 | 2020-06-02 | Apple Inc. | User activity shortcut suggestions |
US11360641B2 (en) | 2019-06-01 | 2022-06-14 | Apple Inc. | Increasing the relevance of new available information |
US11227599B2 (en) | 2019-06-01 | 2022-01-18 | Apple Inc. | Methods and user interfaces for voice-based control of electronic devices |
WO2021056255A1 (en) | 2019-09-25 | 2021-04-01 | Apple Inc. | Text detection using global geometry estimators |
US11038934B1 (en) | 2020-05-11 | 2021-06-15 | Apple Inc. | Digital assistant hardware abstraction |
US11061543B1 (en) | 2020-05-11 | 2021-07-13 | Apple Inc. | Providing relevant data items based on context |
US11755276B2 (en) | 2020-05-12 | 2023-09-12 | Apple Inc. | Reducing description length based on confidence |
US11490204B2 (en) | 2020-07-20 | 2022-11-01 | Apple Inc. | Multi-device audio adjustment coordination |
US11438683B2 (en) | 2020-07-21 | 2022-09-06 | Apple Inc. | User identification using headphones |
US11514897B2 (en) * | 2020-09-25 | 2022-11-29 | Genesys Telecommunications Laboratories, Inc. | Systems and methods relating to bot authoring by mining intents from natural language conversations |
US11265396B1 (en) | 2020-10-01 | 2022-03-01 | Bank Of America Corporation | System for cross channel data caching for performing electronic activities |
US11735207B1 (en) * | 2021-09-30 | 2023-08-22 | Wells Fargo Bank, N.A. | Systems and methods for determining a next action based on weighted predicted emotions, entities, and intents |
CN113742027B (en) * | 2021-11-05 | 2022-07-15 | 深圳传音控股股份有限公司 | Interaction method, intelligent terminal and readable storage medium |
US11880307B2 (en) | 2022-06-25 | 2024-01-23 | Bank Of America Corporation | Systems and methods for dynamic management of stored cache data based on predictive usage information |
Citations (150)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5077804A (en) * | 1990-12-11 | 1991-12-31 | Richard Dnaiel D | Telecommunications device and related method |
US5278980A (en) * | 1991-08-16 | 1994-01-11 | Xerox Corporation | Iterative technique for phrase query formation and an information retrieval system employing same |
US5418717A (en) * | 1990-08-27 | 1995-05-23 | Su; Keh-Yih | Multiple score language processing system |
US5457768A (en) * | 1991-08-13 | 1995-10-10 | Kabushiki Kaisha Toshiba | Speech recognition apparatus using syntactic and semantic analysis |
US5597312A (en) * | 1994-05-04 | 1997-01-28 | U S West Technologies, Inc. | Intelligent tutoring method and system |
US5625748A (en) * | 1994-04-18 | 1997-04-29 | Bbn Corporation | Topic discriminator using posterior probability or confidence scores |
US5671329A (en) * | 1993-03-09 | 1997-09-23 | Nec Corporation | Speech dialogue system in which a recognition and understanding process, application process, and voice input response are performed simultaneously with voice input |
US5867817A (en) * | 1996-08-19 | 1999-02-02 | Virtual Vision, Inc. | Speech recognition manager |
US5937384A (en) * | 1996-05-01 | 1999-08-10 | Microsoft Corporation | Method and system for speech recognition using continuous density hidden Markov models |
US5966686A (en) * | 1996-06-28 | 1999-10-12 | Microsoft Corporation | Method and system for computing semantic logical forms from syntax trees |
US6178398B1 (en) * | 1997-11-18 | 2001-01-23 | Motorola, Inc. | Method, device and system for noise-tolerant language understanding |
US6185531B1 (en) * | 1997-01-09 | 2001-02-06 | Gte Internetworking Incorporated | Topic indexing method |
US6223150B1 (en) * | 1999-01-29 | 2001-04-24 | Sony Corporation | Method and apparatus for parsing in a spoken language translation system |
US6236968B1 (en) * | 1998-05-14 | 2001-05-22 | International Business Machines Corporation | Sleep prevention dialog based car system |
US6243669B1 (en) * | 1999-01-29 | 2001-06-05 | Sony Corporation | Method and apparatus for providing syntactic analysis and data structure for translation knowledge in example-based language translation |
US20010047262A1 (en) * | 2000-02-04 | 2001-11-29 | Alexander Kurganov | Robust voice browser system and voice activated device controller |
US20020002454A1 (en) * | 1998-12-07 | 2002-01-03 | Srinivas Bangalore | Automatic clustering of tokens from a corpus for grammar acquisition |
US20020029304A1 (en) * | 2000-06-06 | 2002-03-07 | Microsoft Corporation | Method and system for defining semantic categories and actions |
US20020032561A1 (en) * | 2000-09-11 | 2002-03-14 | Nec Corporation | Automatic interpreting system, automatic interpreting method, and program for automatic interpreting |
US20020059066A1 (en) * | 1998-04-08 | 2002-05-16 | O'hagan Timothy P. | Speech recognition system and method for employing the same |
US6442524B1 (en) * | 1999-01-29 | 2002-08-27 | Sony Corporation | Analyzing inflectional morphology in a spoken language translation system |
US20020128821A1 (en) * | 1999-05-28 | 2002-09-12 | Farzad Ehsani | Phrase-based dialogue modeling with particular application to creating recognition grammars for voice-controlled user interfaces |
US20020169596A1 (en) * | 2001-05-04 | 2002-11-14 | Brill Eric D. | Method and apparatus for unsupervised training of natural language processing units |
US6519562B1 (en) * | 1999-02-25 | 2003-02-11 | Speechworks International, Inc. | Dynamic semantic control of a speech recognition system |
US20030033333A1 (en) * | 2001-05-11 | 2003-02-13 | Fujitsu Limited | Hot topic extraction apparatus and method, storage medium therefor |
US20030105638A1 (en) * | 2001-11-27 | 2003-06-05 | Taira Rick K. | Method and system for creating computer-understandable structured medical data from natural language reports |
US20030108334A1 (en) * | 2001-12-06 | 2003-06-12 | Koninklijke Philips Elecronics N.V. | Adaptive environment system and method of providing an adaptive environment |
US20030163321A1 (en) * | 2000-06-16 | 2003-08-28 | Mault James R | Speech recognition capability for a personal digital assistant |
US20030233224A1 (en) * | 2001-08-14 | 2003-12-18 | Insightful Corporation | Method and system for enhanced data searching |
US6697793B2 (en) * | 2001-03-02 | 2004-02-24 | The United States Of America As Represented By The Administrator Of The National Aeronautics And Space Administration | System, method and apparatus for generating phrases from a database |
US20040059577A1 (en) * | 2002-06-28 | 2004-03-25 | International Business Machines Corporation | Method and apparatus for preparing a document to be read by a text-to-speech reader |
US20040078190A1 (en) * | 2000-09-29 | 2004-04-22 | Fass Daniel C | Method and system for describing and identifying concepts in natural language text for information retrieval and processing |
US6766320B1 (en) * | 2000-08-24 | 2004-07-20 | Microsoft Corporation | Search engine with natural language-based robust parsing for user query and relevance feedback learning |
US20040148170A1 (en) * | 2003-01-23 | 2004-07-29 | Alejandro Acero | Statistical classifiers for spoken language understanding and command/control scenarios |
US20040158558A1 (en) * | 2002-11-26 | 2004-08-12 | Atsuko Koizumi | Information processor and program for implementing information processor |
US6816831B1 (en) * | 1999-10-28 | 2004-11-09 | Sony Corporation | Language learning apparatus and method therefor |
US20050049867A1 (en) * | 2003-08-11 | 2005-03-03 | Paul Deane | Cooccurrence and constructions |
US20050055209A1 (en) * | 2003-09-05 | 2005-03-10 | Epstein Mark E. | Semantic language modeling and confidence measurement |
US20050080629A1 (en) * | 2002-01-18 | 2005-04-14 | David Attwater | Multi-mode interactive dialogue apparatus and method |
US20050105712A1 (en) * | 2003-02-11 | 2005-05-19 | Williams David R. | Machine learning |
US20050144000A1 (en) * | 2003-12-26 | 2005-06-30 | Kabushiki Kaisha Toshiba | Contents providing apparatus and method |
US6937975B1 (en) * | 1998-10-08 | 2005-08-30 | Canon Kabushiki Kaisha | Apparatus and method for processing natural language |
US6961954B1 (en) * | 1997-10-27 | 2005-11-01 | The Mitre Corporation | Automated segmentation, information extraction, summarization, and presentation of broadcast news |
US7006973B1 (en) * | 2000-01-31 | 2006-02-28 | Intel Corporation | Providing information in response to spoken requests |
US20060056602A1 (en) * | 2004-09-13 | 2006-03-16 | Sbc Knowledge Ventures, L.P. | System and method for analysis and adjustment of speech-enabled systems |
US20060074671A1 (en) * | 2004-10-05 | 2006-04-06 | Gary Farmaner | System and methods for improving accuracy of speech recognition |
US20060080098A1 (en) * | 2004-09-30 | 2006-04-13 | Nick Campbell | Apparatus and method for speech processing using paralinguistic information in vector form |
US20060106596A1 (en) * | 2000-07-20 | 2006-05-18 | Microsoft Corporation | Ranking Parser for a Natural Language Processing System |
US20060129397A1 (en) * | 2004-12-10 | 2006-06-15 | Microsoft Corporation | System and method for identifying semantic intent from acoustic information |
US7085708B2 (en) * | 2000-09-23 | 2006-08-01 | Ravenflow, Inc. | Computer system with natural language to machine language translator |
US20060173686A1 (en) * | 2005-02-01 | 2006-08-03 | Samsung Electronics Co., Ltd. | Apparatus, method, and medium for generating grammar network for use in speech recognition and dialogue speech recognition |
US20060173683A1 (en) * | 2005-02-03 | 2006-08-03 | Voice Signal Technologies, Inc. | Methods and apparatus for automatically extending the voice vocabulary of mobile communications devices |
US7089226B1 (en) * | 2001-06-28 | 2006-08-08 | Microsoft Corporation | System, representation, and method providing multilevel information retrieval with clarification dialog |
US20060197764A1 (en) * | 2005-03-02 | 2006-09-07 | Yang George L | Document animation system |
US20060235843A1 (en) * | 2005-01-31 | 2006-10-19 | Textdigger, Inc. | Method and system for semantic search and retrieval of electronic documents |
US20060259299A1 (en) * | 2003-01-15 | 2006-11-16 | Yumiko Kato | Broadcast reception method, broadcast reception systm, recording medium and program (as amended) |
US20060259294A1 (en) * | 2002-12-16 | 2006-11-16 | John Tashereau | Voice recognition system and method |
US20070005369A1 (en) * | 2005-06-30 | 2007-01-04 | Microsoft Corporation | Dialog analysis |
US7177817B1 (en) * | 2002-12-12 | 2007-02-13 | Tuvox Incorporated | Automatic generation of voice content for a voice response system |
US20070118374A1 (en) * | 2005-11-23 | 2007-05-24 | Wise Gerald B | Method for generating closed captions |
US20070136048A1 (en) * | 2005-12-13 | 2007-06-14 | David Richardson-Bunbury | System for classifying words |
US20070156747A1 (en) * | 2005-12-12 | 2007-07-05 | Tegic Communications Llc | Mobile Device Retrieval and Navigation |
US20070174057A1 (en) * | 2000-01-31 | 2007-07-26 | Genly Christopher H | Providing programming information in response to spoken requests |
US20070225980A1 (en) * | 2006-03-24 | 2007-09-27 | Kabushiki Kaisha Toshiba | Apparatus, method and computer program product for recognizing speech |
US7313523B1 (en) * | 2003-05-14 | 2007-12-25 | Apple Inc. | Method and apparatus for assigning word prominence to new or previous information in speech synthesis |
US20080046244A1 (en) * | 2004-11-30 | 2008-02-21 | Yoshio Ohno | Speech Recognition Device |
US20080071536A1 (en) * | 2006-09-15 | 2008-03-20 | Honda Motor Co., Ltd. | Voice recognition device, voice recognition method, and voice recognition program |
US20080140389A1 (en) * | 2006-12-06 | 2008-06-12 | Honda Motor Co., Ltd. | Language understanding apparatus, language understanding method, and computer program |
US20080141125A1 (en) * | 2006-06-23 | 2008-06-12 | Firooz Ghassabian | Combined data entry systems |
US20080154870A1 (en) * | 2006-12-26 | 2008-06-26 | Voice Signal Technologies, Inc. | Collection and use of side information in voice-mediated mobile search |
US20080201136A1 (en) * | 2007-02-19 | 2008-08-21 | Kabushiki Kaisha Toshiba | Apparatus and Method for Speech Recognition |
US20080221903A1 (en) * | 2005-08-31 | 2008-09-11 | International Business Machines Corporation | Hierarchical Methods and Apparatus for Extracting User Intent from Spoken Utterances |
US20080243820A1 (en) * | 2007-03-27 | 2008-10-02 | Walter Chang | Semantic analysis documents to rank terms |
US20080319748A1 (en) * | 2006-01-31 | 2008-12-25 | Mikio Nakano | Conversation System and Conversation Software |
US20090006343A1 (en) * | 2007-06-28 | 2009-01-01 | Microsoft Corporation | Machine assisted query formulation |
US20090006345A1 (en) * | 2007-06-28 | 2009-01-01 | Microsoft Corporation | Voice-based search processing |
US20090063426A1 (en) * | 2007-08-31 | 2009-03-05 | Powerset, Inc. | Identification of semantic relationships within reported speech |
US20090077047A1 (en) * | 2006-08-14 | 2009-03-19 | Inquira, Inc. | Method and apparatus for identifying and classifying query intent |
US20090094233A1 (en) * | 2007-10-05 | 2009-04-09 | Fujitsu Limited | Modeling Topics Using Statistical Distributions |
US20090150156A1 (en) * | 2007-12-11 | 2009-06-11 | Kennewick Michael R | System and method for providing a natural language voice user interface in an integrated voice navigation services environment |
US20090209345A1 (en) * | 2008-02-14 | 2009-08-20 | Aruze Gaming America, Inc. | Multiplayer participation type gaming system limiting dialogue voices outputted from gaming machine |
US20090259650A1 (en) * | 2008-04-11 | 2009-10-15 | Ebay Inc. | System and method for identification of near duplicate user-generated content |
US20090276419A1 (en) * | 2008-05-01 | 2009-11-05 | Chacha Search Inc. | Method and system for improvement of request processing |
US20090285474A1 (en) * | 2008-05-15 | 2009-11-19 | Berteau Stefan A | System and Method for Bayesian Text Classification |
US20090313227A1 (en) * | 2008-06-14 | 2009-12-17 | Veoh Networks, Inc. | Searching Using Patterns of Usage |
US20090327260A1 (en) * | 2008-06-25 | 2009-12-31 | Microsoft Corporation | Constructing a classifier for classifying queries |
US20100023506A1 (en) * | 2008-07-22 | 2010-01-28 | Yahoo! Inc. | Augmenting online content with additional content relevant to user interests |
US20100057687A1 (en) * | 2008-09-04 | 2010-03-04 | Microsoft Corporation | Predicting future queries from log data |
US20100094854A1 (en) * | 2008-10-14 | 2010-04-15 | Omid Rouhani-Kalleh | System for automatically categorizing queries |
US20100114908A1 (en) * | 2008-11-04 | 2010-05-06 | Microsoft Corporation | Relevant navigation with deep links into query |
US20100121840A1 (en) * | 2008-11-12 | 2010-05-13 | Yahoo! Inc. | Query difficulty estimation |
US20100131835A1 (en) * | 2008-11-22 | 2010-05-27 | Srihari Kumar | System and methods for inferring intent of website visitors and generating and packaging visitor information for distribution as sales leads or market intelligence |
US20100138402A1 (en) * | 2008-12-02 | 2010-06-03 | Chacha Search, Inc. | Method and system for improving utilization of human searchers |
US20100145710A1 (en) * | 2008-12-08 | 2010-06-10 | Nuance Communications, Inc. | Data-Driven Voice User Interface |
US7739103B2 (en) * | 2004-04-06 | 2010-06-15 | Educational Testing Service | Lexical association metric for knowledge-free extraction of phrasal terms |
US20100153106A1 (en) * | 2008-12-15 | 2010-06-17 | Verizon Data Services Llc | Conversation mapping |
US20100153317A1 (en) * | 2008-12-11 | 2010-06-17 | Samsung Electronics Co., Ltd | Intelligent robot and control method thereof |
US20100174716A1 (en) * | 2004-09-30 | 2010-07-08 | Google Inc. | Methods and systems for improving text segmentation |
US20100228762A1 (en) * | 2009-03-05 | 2010-09-09 | Mauge Karin | System and method to provide query linguistic service |
US20100268536A1 (en) * | 2009-04-17 | 2010-10-21 | David Suendermann | System and method for improving performance of semantic classifiers in spoken dialog systems |
US20100293174A1 (en) * | 2009-05-12 | 2010-11-18 | Microsoft Corporation | Query classification |
US20110004462A1 (en) * | 2009-07-01 | 2011-01-06 | Comcast Interactive Media, Llc | Generating Topic-Specific Language Models |
US20110010367A1 (en) * | 2009-06-11 | 2011-01-13 | Chacha Search, Inc. | Method and system of providing a search tool |
US20110029311A1 (en) * | 2009-07-30 | 2011-02-03 | Sony Corporation | Voice processing device and method, and program |
US20110029533A1 (en) * | 2009-07-28 | 2011-02-03 | Prasantha Jayakody | Method and system for tag suggestion in a tag-associated data-object storage system |
US7912702B2 (en) * | 1999-11-12 | 2011-03-22 | Phoenix Solutions, Inc. | Statistical language model trained with semantic variants |
US7912701B1 (en) * | 2005-05-04 | 2011-03-22 | IgniteIP Capital IA Special Management LLC | Method and apparatus for semiotic correlation |
US20110099003A1 (en) * | 2009-10-28 | 2011-04-28 | Masaaki Isozu | Information processing apparatus, information processing method, and program |
US7983896B2 (en) * | 2004-03-05 | 2011-07-19 | SDL Language Technology | In-context exact (ICE) matching |
US20110184730A1 (en) * | 2010-01-22 | 2011-07-28 | Google Inc. | Multi-dimensional disambiguation of voice commands |
US20110191423A1 (en) * | 2010-01-29 | 2011-08-04 | Mcafee, Inc. | Reputation management for network content classification |
US7996219B2 (en) * | 2005-03-21 | 2011-08-09 | At&T Intellectual Property Ii, L.P. | Apparatus and method for model adaptation for spoken language understanding |
US20110213777A1 (en) * | 2010-02-01 | 2011-09-01 | Alibaba Group Holding Limited | Method and Apparatus of Text Classification |
US20110225019A1 (en) * | 2008-10-14 | 2011-09-15 | David Taylor | Search, analysis and categorization |
US20110238410A1 (en) * | 2010-03-26 | 2011-09-29 | Jean-Marie Henri Daniel Larcheveque | Semantic Clustering and User Interfaces |
US20110238408A1 (en) * | 2010-03-26 | 2011-09-29 | Jean-Marie Henri Daniel Larcheveque | Semantic Clustering |
US20110258204A1 (en) * | 2007-01-19 | 2011-10-20 | Wordnetworks, Inc. | System for using keyword phrases on a page to provide contextually relevant content to users |
US20110282913A1 (en) * | 2009-04-30 | 2011-11-17 | Oki Electric Industry Co., Ltd. | Dialogue control system, method and computer readable storage medium, and multidimensional ontology processing system, method and computer readable storage medium |
US20110288868A1 (en) * | 2010-05-19 | 2011-11-24 | Lloyd Matthew I | Disambiguation of contact information using historical data |
US20110301943A1 (en) * | 2007-05-17 | 2011-12-08 | Redstart Systems, Inc. | System and method of dictation for a speech recognition command system |
US20110301955A1 (en) * | 2010-06-07 | 2011-12-08 | Google Inc. | Predicting and Learning Carrier Phrases for Speech Input |
US20110314012A1 (en) * | 2010-06-16 | 2011-12-22 | Microsoft Corporation | Determining query intent |
US20110314390A1 (en) * | 2010-06-18 | 2011-12-22 | Microsoft Corporation | Techniques to dynamically modify themes based on messaging |
US20120016873A1 (en) * | 2010-07-16 | 2012-01-19 | Michael Mathieson | Method and system for ranking search results based on categories |
US20120053927A1 (en) * | 2010-09-01 | 2012-03-01 | Microsoft Corporation | Identifying topically-related phrases in a browsing sequence |
US20120078919A1 (en) * | 2010-09-29 | 2012-03-29 | Fujitsu Limited | Comparison of character strings |
US20120084291A1 (en) * | 2010-09-30 | 2012-04-05 | Microsoft Corporation | Applying search queries to content sets |
US20120096033A1 (en) * | 2010-10-14 | 2012-04-19 | Microsoft Corporation | Disambiguation of Entities |
US20120158693A1 (en) * | 2010-12-17 | 2012-06-21 | Yahoo! Inc. | Method and system for generating web pages for topics unassociated with a dominant url |
US20120158703A1 (en) * | 2010-12-16 | 2012-06-21 | Microsoft Corporation | Search lexicon expansion |
US20120191453A1 (en) * | 2010-04-26 | 2012-07-26 | Cyberpulse L.L.C. | System and methods for matching an utterance to a template hierarchy |
US8255383B2 (en) * | 2006-07-14 | 2012-08-28 | Chacha Search, Inc | Method and system for qualifying keywords in query strings |
US8311807B2 (en) * | 2004-11-09 | 2012-11-13 | Samsung Electronics Co., Ltd. | Periodically extracting and evaluating frequency of occurrence data of unregistered terms in a document for updating a dictionary |
US20120290293A1 (en) * | 2011-05-13 | 2012-11-15 | Microsoft Corporation | Exploiting Query Click Logs for Domain Detection in Spoken Language Understanding |
US8321220B1 (en) * | 2005-11-30 | 2012-11-27 | At&T Intellectual Property Ii, L.P. | System and method of semi-supervised learning for spoken language understanding using semantic role labeling |
US20120310628A1 (en) * | 2007-04-25 | 2012-12-06 | Samsung Electronics Co., Ltd. | Method and system for providing access to information of potential interest to a user |
US20120317107A1 (en) * | 2007-10-11 | 2012-12-13 | Google Inc. | Methods and Systems for Classifying Search Results to Determine Page Elements |
US20130006995A1 (en) * | 2009-12-10 | 2013-01-03 | Chesterdeal Limited | Accessing stored electronic resources |
US8370352B2 (en) * | 2007-10-18 | 2013-02-05 | Siemens Medical Solutions Usa, Inc. | Contextual searching of electronic records and visual rule construction |
US20130138641A1 (en) * | 2009-12-30 | 2013-05-30 | Google Inc. | Construction of text classifiers |
US20130212475A1 (en) * | 2010-11-01 | 2013-08-15 | Koninklijke Philips Electronics N.V. | Suggesting relevant terms during text entry |
US8521526B1 (en) * | 2010-07-28 | 2013-08-27 | Google Inc. | Disambiguation of a spoken query term |
US8527262B2 (en) * | 2007-06-22 | 2013-09-03 | International Business Machines Corporation | Systems and methods for automatic semantic role labeling of high morphological text for natural language processing applications |
US8560321B1 (en) * | 2011-01-05 | 2013-10-15 | Interactions Corportion | Automated speech recognition system for natural language understanding |
US20140143054A1 (en) * | 2005-10-31 | 2014-05-22 | Yahoo! Inc. | System for identifying and selecting advertising categories |
US20140149399A1 (en) * | 2010-07-22 | 2014-05-29 | Google Inc. | Determining user intent from query patterns |
US9002725B1 (en) * | 2005-04-20 | 2015-04-07 | Google Inc. | System and method for targeting information based on message content |
US9093073B1 (en) * | 2007-02-12 | 2015-07-28 | West Corporation | Automatic speech recognition tagging |
US9177045B2 (en) * | 2010-06-02 | 2015-11-03 | Microsoft Technology Licensing, Llc | Topical search engines and query context models |
US9330168B1 (en) * | 2010-02-19 | 2016-05-03 | Go Daddy Operating Company, LLC | System and method for identifying website verticals |
Family Cites Families (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US7366666B2 (en) * | 2003-10-01 | 2008-04-29 | International Business Machines Corporation | Relative delta computations for determining the meaning of language inputs |
US8380511B2 (en) * | 2007-02-20 | 2013-02-19 | Intervoice Limited Partnership | System and method for semantic categorization |
US8229743B2 (en) * | 2009-06-23 | 2012-07-24 | Autonomy Corporation Ltd. | Speech recognition system |
-
2011
- 2011-02-28 US US13/037,114 patent/US8688453B1/en active Active
-
2014
- 2014-02-19 US US14/184,379 patent/US20140180692A1/en not_active Abandoned
Patent Citations (151)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5418717A (en) * | 1990-08-27 | 1995-05-23 | Su; Keh-Yih | Multiple score language processing system |
US5077804A (en) * | 1990-12-11 | 1991-12-31 | Richard Dnaiel D | Telecommunications device and related method |
US5457768A (en) * | 1991-08-13 | 1995-10-10 | Kabushiki Kaisha Toshiba | Speech recognition apparatus using syntactic and semantic analysis |
US5278980A (en) * | 1991-08-16 | 1994-01-11 | Xerox Corporation | Iterative technique for phrase query formation and an information retrieval system employing same |
US5671329A (en) * | 1993-03-09 | 1997-09-23 | Nec Corporation | Speech dialogue system in which a recognition and understanding process, application process, and voice input response are performed simultaneously with voice input |
US5625748A (en) * | 1994-04-18 | 1997-04-29 | Bbn Corporation | Topic discriminator using posterior probability or confidence scores |
US5597312A (en) * | 1994-05-04 | 1997-01-28 | U S West Technologies, Inc. | Intelligent tutoring method and system |
US5937384A (en) * | 1996-05-01 | 1999-08-10 | Microsoft Corporation | Method and system for speech recognition using continuous density hidden Markov models |
US5966686A (en) * | 1996-06-28 | 1999-10-12 | Microsoft Corporation | Method and system for computing semantic logical forms from syntax trees |
US5867817A (en) * | 1996-08-19 | 1999-02-02 | Virtual Vision, Inc. | Speech recognition manager |
US6185531B1 (en) * | 1997-01-09 | 2001-02-06 | Gte Internetworking Incorporated | Topic indexing method |
US6961954B1 (en) * | 1997-10-27 | 2005-11-01 | The Mitre Corporation | Automated segmentation, information extraction, summarization, and presentation of broadcast news |
US6178398B1 (en) * | 1997-11-18 | 2001-01-23 | Motorola, Inc. | Method, device and system for noise-tolerant language understanding |
US20020059066A1 (en) * | 1998-04-08 | 2002-05-16 | O'hagan Timothy P. | Speech recognition system and method for employing the same |
US6236968B1 (en) * | 1998-05-14 | 2001-05-22 | International Business Machines Corporation | Sleep prevention dialog based car system |
US6937975B1 (en) * | 1998-10-08 | 2005-08-30 | Canon Kabushiki Kaisha | Apparatus and method for processing natural language |
US20020002454A1 (en) * | 1998-12-07 | 2002-01-03 | Srinivas Bangalore | Automatic clustering of tokens from a corpus for grammar acquisition |
US6243669B1 (en) * | 1999-01-29 | 2001-06-05 | Sony Corporation | Method and apparatus for providing syntactic analysis and data structure for translation knowledge in example-based language translation |
US6442524B1 (en) * | 1999-01-29 | 2002-08-27 | Sony Corporation | Analyzing inflectional morphology in a spoken language translation system |
US6223150B1 (en) * | 1999-01-29 | 2001-04-24 | Sony Corporation | Method and apparatus for parsing in a spoken language translation system |
US6519562B1 (en) * | 1999-02-25 | 2003-02-11 | Speechworks International, Inc. | Dynamic semantic control of a speech recognition system |
US20020128821A1 (en) * | 1999-05-28 | 2002-09-12 | Farzad Ehsani | Phrase-based dialogue modeling with particular application to creating recognition grammars for voice-controlled user interfaces |
US6816831B1 (en) * | 1999-10-28 | 2004-11-09 | Sony Corporation | Language learning apparatus and method therefor |
US7912702B2 (en) * | 1999-11-12 | 2011-03-22 | Phoenix Solutions, Inc. | Statistical language model trained with semantic variants |
US7006973B1 (en) * | 2000-01-31 | 2006-02-28 | Intel Corporation | Providing information in response to spoken requests |
US20070174057A1 (en) * | 2000-01-31 | 2007-07-26 | Genly Christopher H | Providing programming information in response to spoken requests |
US20010047262A1 (en) * | 2000-02-04 | 2001-11-29 | Alexander Kurganov | Robust voice browser system and voice activated device controller |
US20020029304A1 (en) * | 2000-06-06 | 2002-03-07 | Microsoft Corporation | Method and system for defining semantic categories and actions |
US20030163321A1 (en) * | 2000-06-16 | 2003-08-28 | Mault James R | Speech recognition capability for a personal digital assistant |
US20060106596A1 (en) * | 2000-07-20 | 2006-05-18 | Microsoft Corporation | Ranking Parser for a Natural Language Processing System |
US6766320B1 (en) * | 2000-08-24 | 2004-07-20 | Microsoft Corporation | Search engine with natural language-based robust parsing for user query and relevance feedback learning |
US20020032561A1 (en) * | 2000-09-11 | 2002-03-14 | Nec Corporation | Automatic interpreting system, automatic interpreting method, and program for automatic interpreting |
US7085708B2 (en) * | 2000-09-23 | 2006-08-01 | Ravenflow, Inc. | Computer system with natural language to machine language translator |
US20040078190A1 (en) * | 2000-09-29 | 2004-04-22 | Fass Daniel C | Method and system for describing and identifying concepts in natural language text for information retrieval and processing |
US6697793B2 (en) * | 2001-03-02 | 2004-02-24 | The United States Of America As Represented By The Administrator Of The National Aeronautics And Space Administration | System, method and apparatus for generating phrases from a database |
US20020169596A1 (en) * | 2001-05-04 | 2002-11-14 | Brill Eric D. | Method and apparatus for unsupervised training of natural language processing units |
US20030033333A1 (en) * | 2001-05-11 | 2003-02-13 | Fujitsu Limited | Hot topic extraction apparatus and method, storage medium therefor |
US7089226B1 (en) * | 2001-06-28 | 2006-08-08 | Microsoft Corporation | System, representation, and method providing multilevel information retrieval with clarification dialog |
US20030233224A1 (en) * | 2001-08-14 | 2003-12-18 | Insightful Corporation | Method and system for enhanced data searching |
US20030105638A1 (en) * | 2001-11-27 | 2003-06-05 | Taira Rick K. | Method and system for creating computer-understandable structured medical data from natural language reports |
US20030108334A1 (en) * | 2001-12-06 | 2003-06-12 | Koninklijke Philips Elecronics N.V. | Adaptive environment system and method of providing an adaptive environment |
US20050080629A1 (en) * | 2002-01-18 | 2005-04-14 | David Attwater | Multi-mode interactive dialogue apparatus and method |
US20040059577A1 (en) * | 2002-06-28 | 2004-03-25 | International Business Machines Corporation | Method and apparatus for preparing a document to be read by a text-to-speech reader |
US20040158558A1 (en) * | 2002-11-26 | 2004-08-12 | Atsuko Koizumi | Information processor and program for implementing information processor |
US7177817B1 (en) * | 2002-12-12 | 2007-02-13 | Tuvox Incorporated | Automatic generation of voice content for a voice response system |
US20060259294A1 (en) * | 2002-12-16 | 2006-11-16 | John Tashereau | Voice recognition system and method |
US20060259299A1 (en) * | 2003-01-15 | 2006-11-16 | Yumiko Kato | Broadcast reception method, broadcast reception systm, recording medium and program (as amended) |
US20040148170A1 (en) * | 2003-01-23 | 2004-07-29 | Alejandro Acero | Statistical classifiers for spoken language understanding and command/control scenarios |
US20050105712A1 (en) * | 2003-02-11 | 2005-05-19 | Williams David R. | Machine learning |
US7313523B1 (en) * | 2003-05-14 | 2007-12-25 | Apple Inc. | Method and apparatus for assigning word prominence to new or previous information in speech synthesis |
US20050049867A1 (en) * | 2003-08-11 | 2005-03-03 | Paul Deane | Cooccurrence and constructions |
US20050055209A1 (en) * | 2003-09-05 | 2005-03-10 | Epstein Mark E. | Semantic language modeling and confidence measurement |
US20050144000A1 (en) * | 2003-12-26 | 2005-06-30 | Kabushiki Kaisha Toshiba | Contents providing apparatus and method |
US7983896B2 (en) * | 2004-03-05 | 2011-07-19 | SDL Language Technology | In-context exact (ICE) matching |
US7739103B2 (en) * | 2004-04-06 | 2010-06-15 | Educational Testing Service | Lexical association metric for knowledge-free extraction of phrasal terms |
US20060056602A1 (en) * | 2004-09-13 | 2006-03-16 | Sbc Knowledge Ventures, L.P. | System and method for analysis and adjustment of speech-enabled systems |
US20100174716A1 (en) * | 2004-09-30 | 2010-07-08 | Google Inc. | Methods and systems for improving text segmentation |
US20060080098A1 (en) * | 2004-09-30 | 2006-04-13 | Nick Campbell | Apparatus and method for speech processing using paralinguistic information in vector form |
US20060074671A1 (en) * | 2004-10-05 | 2006-04-06 | Gary Farmaner | System and methods for improving accuracy of speech recognition |
US8311807B2 (en) * | 2004-11-09 | 2012-11-13 | Samsung Electronics Co., Ltd. | Periodically extracting and evaluating frequency of occurrence data of unregistered terms in a document for updating a dictionary |
US20080046244A1 (en) * | 2004-11-30 | 2008-02-21 | Yoshio Ohno | Speech Recognition Device |
US20060129397A1 (en) * | 2004-12-10 | 2006-06-15 | Microsoft Corporation | System and method for identifying semantic intent from acoustic information |
US20060235843A1 (en) * | 2005-01-31 | 2006-10-19 | Textdigger, Inc. | Method and system for semantic search and retrieval of electronic documents |
US20060173686A1 (en) * | 2005-02-01 | 2006-08-03 | Samsung Electronics Co., Ltd. | Apparatus, method, and medium for generating grammar network for use in speech recognition and dialogue speech recognition |
US20060173683A1 (en) * | 2005-02-03 | 2006-08-03 | Voice Signal Technologies, Inc. | Methods and apparatus for automatically extending the voice vocabulary of mobile communications devices |
US20060197764A1 (en) * | 2005-03-02 | 2006-09-07 | Yang George L | Document animation system |
US7996219B2 (en) * | 2005-03-21 | 2011-08-09 | At&T Intellectual Property Ii, L.P. | Apparatus and method for model adaptation for spoken language understanding |
US9002725B1 (en) * | 2005-04-20 | 2015-04-07 | Google Inc. | System and method for targeting information based on message content |
US7912701B1 (en) * | 2005-05-04 | 2011-03-22 | IgniteIP Capital IA Special Management LLC | Method and apparatus for semiotic correlation |
US20070005369A1 (en) * | 2005-06-30 | 2007-01-04 | Microsoft Corporation | Dialog analysis |
US20080221903A1 (en) * | 2005-08-31 | 2008-09-11 | International Business Machines Corporation | Hierarchical Methods and Apparatus for Extracting User Intent from Spoken Utterances |
US20140143054A1 (en) * | 2005-10-31 | 2014-05-22 | Yahoo! Inc. | System for identifying and selecting advertising categories |
US20070118374A1 (en) * | 2005-11-23 | 2007-05-24 | Wise Gerald B | Method for generating closed captions |
US8321220B1 (en) * | 2005-11-30 | 2012-11-27 | At&T Intellectual Property Ii, L.P. | System and method of semi-supervised learning for spoken language understanding using semantic role labeling |
US20070156747A1 (en) * | 2005-12-12 | 2007-07-05 | Tegic Communications Llc | Mobile Device Retrieval and Navigation |
US20070136048A1 (en) * | 2005-12-13 | 2007-06-14 | David Richardson-Bunbury | System for classifying words |
US20080319748A1 (en) * | 2006-01-31 | 2008-12-25 | Mikio Nakano | Conversation System and Conversation Software |
US20070225980A1 (en) * | 2006-03-24 | 2007-09-27 | Kabushiki Kaisha Toshiba | Apparatus, method and computer program product for recognizing speech |
US20080141125A1 (en) * | 2006-06-23 | 2008-06-12 | Firooz Ghassabian | Combined data entry systems |
US8255383B2 (en) * | 2006-07-14 | 2012-08-28 | Chacha Search, Inc | Method and system for qualifying keywords in query strings |
US20090077047A1 (en) * | 2006-08-14 | 2009-03-19 | Inquira, Inc. | Method and apparatus for identifying and classifying query intent |
US20080071536A1 (en) * | 2006-09-15 | 2008-03-20 | Honda Motor Co., Ltd. | Voice recognition device, voice recognition method, and voice recognition program |
US20080140389A1 (en) * | 2006-12-06 | 2008-06-12 | Honda Motor Co., Ltd. | Language understanding apparatus, language understanding method, and computer program |
US20080154870A1 (en) * | 2006-12-26 | 2008-06-26 | Voice Signal Technologies, Inc. | Collection and use of side information in voice-mediated mobile search |
US20110258204A1 (en) * | 2007-01-19 | 2011-10-20 | Wordnetworks, Inc. | System for using keyword phrases on a page to provide contextually relevant content to users |
US9093073B1 (en) * | 2007-02-12 | 2015-07-28 | West Corporation | Automatic speech recognition tagging |
US20080201136A1 (en) * | 2007-02-19 | 2008-08-21 | Kabushiki Kaisha Toshiba | Apparatus and Method for Speech Recognition |
US20080243820A1 (en) * | 2007-03-27 | 2008-10-02 | Walter Chang | Semantic analysis documents to rank terms |
US20120310628A1 (en) * | 2007-04-25 | 2012-12-06 | Samsung Electronics Co., Ltd. | Method and system for providing access to information of potential interest to a user |
US20110301943A1 (en) * | 2007-05-17 | 2011-12-08 | Redstart Systems, Inc. | System and method of dictation for a speech recognition command system |
US8527262B2 (en) * | 2007-06-22 | 2013-09-03 | International Business Machines Corporation | Systems and methods for automatic semantic role labeling of high morphological text for natural language processing applications |
US20090006343A1 (en) * | 2007-06-28 | 2009-01-01 | Microsoft Corporation | Machine assisted query formulation |
US20090006345A1 (en) * | 2007-06-28 | 2009-01-01 | Microsoft Corporation | Voice-based search processing |
US20090063426A1 (en) * | 2007-08-31 | 2009-03-05 | Powerset, Inc. | Identification of semantic relationships within reported speech |
US9317593B2 (en) * | 2007-10-05 | 2016-04-19 | Fujitsu Limited | Modeling topics using statistical distributions |
US20090094233A1 (en) * | 2007-10-05 | 2009-04-09 | Fujitsu Limited | Modeling Topics Using Statistical Distributions |
US20120317107A1 (en) * | 2007-10-11 | 2012-12-13 | Google Inc. | Methods and Systems for Classifying Search Results to Determine Page Elements |
US8370352B2 (en) * | 2007-10-18 | 2013-02-05 | Siemens Medical Solutions Usa, Inc. | Contextual searching of electronic records and visual rule construction |
US20090150156A1 (en) * | 2007-12-11 | 2009-06-11 | Kennewick Michael R | System and method for providing a natural language voice user interface in an integrated voice navigation services environment |
US20090209345A1 (en) * | 2008-02-14 | 2009-08-20 | Aruze Gaming America, Inc. | Multiplayer participation type gaming system limiting dialogue voices outputted from gaming machine |
US20090259650A1 (en) * | 2008-04-11 | 2009-10-15 | Ebay Inc. | System and method for identification of near duplicate user-generated content |
US20090276419A1 (en) * | 2008-05-01 | 2009-11-05 | Chacha Search Inc. | Method and system for improvement of request processing |
US20090285474A1 (en) * | 2008-05-15 | 2009-11-19 | Berteau Stefan A | System and Method for Bayesian Text Classification |
US20090313227A1 (en) * | 2008-06-14 | 2009-12-17 | Veoh Networks, Inc. | Searching Using Patterns of Usage |
US20090327260A1 (en) * | 2008-06-25 | 2009-12-31 | Microsoft Corporation | Constructing a classifier for classifying queries |
US20100023506A1 (en) * | 2008-07-22 | 2010-01-28 | Yahoo! Inc. | Augmenting online content with additional content relevant to user interests |
US20100057687A1 (en) * | 2008-09-04 | 2010-03-04 | Microsoft Corporation | Predicting future queries from log data |
US20100094854A1 (en) * | 2008-10-14 | 2010-04-15 | Omid Rouhani-Kalleh | System for automatically categorizing queries |
US20110225019A1 (en) * | 2008-10-14 | 2011-09-15 | David Taylor | Search, analysis and categorization |
US20100114908A1 (en) * | 2008-11-04 | 2010-05-06 | Microsoft Corporation | Relevant navigation with deep links into query |
US20100121840A1 (en) * | 2008-11-12 | 2010-05-13 | Yahoo! Inc. | Query difficulty estimation |
US20100131835A1 (en) * | 2008-11-22 | 2010-05-27 | Srihari Kumar | System and methods for inferring intent of website visitors and generating and packaging visitor information for distribution as sales leads or market intelligence |
US20100138402A1 (en) * | 2008-12-02 | 2010-06-03 | Chacha Search, Inc. | Method and system for improving utilization of human searchers |
US20100145710A1 (en) * | 2008-12-08 | 2010-06-10 | Nuance Communications, Inc. | Data-Driven Voice User Interface |
US20100153317A1 (en) * | 2008-12-11 | 2010-06-17 | Samsung Electronics Co., Ltd | Intelligent robot and control method thereof |
US20100153106A1 (en) * | 2008-12-15 | 2010-06-17 | Verizon Data Services Llc | Conversation mapping |
US20100228762A1 (en) * | 2009-03-05 | 2010-09-09 | Mauge Karin | System and method to provide query linguistic service |
US20100268536A1 (en) * | 2009-04-17 | 2010-10-21 | David Suendermann | System and method for improving performance of semantic classifiers in spoken dialog systems |
US20110282913A1 (en) * | 2009-04-30 | 2011-11-17 | Oki Electric Industry Co., Ltd. | Dialogue control system, method and computer readable storage medium, and multidimensional ontology processing system, method and computer readable storage medium |
US20100293174A1 (en) * | 2009-05-12 | 2010-11-18 | Microsoft Corporation | Query classification |
US20110010367A1 (en) * | 2009-06-11 | 2011-01-13 | Chacha Search, Inc. | Method and system of providing a search tool |
US20110004462A1 (en) * | 2009-07-01 | 2011-01-06 | Comcast Interactive Media, Llc | Generating Topic-Specific Language Models |
US20110029533A1 (en) * | 2009-07-28 | 2011-02-03 | Prasantha Jayakody | Method and system for tag suggestion in a tag-associated data-object storage system |
US20110029311A1 (en) * | 2009-07-30 | 2011-02-03 | Sony Corporation | Voice processing device and method, and program |
US20110099003A1 (en) * | 2009-10-28 | 2011-04-28 | Masaaki Isozu | Information processing apparatus, information processing method, and program |
US20130006995A1 (en) * | 2009-12-10 | 2013-01-03 | Chesterdeal Limited | Accessing stored electronic resources |
US20130138641A1 (en) * | 2009-12-30 | 2013-05-30 | Google Inc. | Construction of text classifiers |
US20110184730A1 (en) * | 2010-01-22 | 2011-07-28 | Google Inc. | Multi-dimensional disambiguation of voice commands |
US20110191423A1 (en) * | 2010-01-29 | 2011-08-04 | Mcafee, Inc. | Reputation management for network content classification |
US20110213777A1 (en) * | 2010-02-01 | 2011-09-01 | Alibaba Group Holding Limited | Method and Apparatus of Text Classification |
US9330168B1 (en) * | 2010-02-19 | 2016-05-03 | Go Daddy Operating Company, LLC | System and method for identifying website verticals |
US20110238410A1 (en) * | 2010-03-26 | 2011-09-29 | Jean-Marie Henri Daniel Larcheveque | Semantic Clustering and User Interfaces |
US20110238408A1 (en) * | 2010-03-26 | 2011-09-29 | Jean-Marie Henri Daniel Larcheveque | Semantic Clustering |
US20120191453A1 (en) * | 2010-04-26 | 2012-07-26 | Cyberpulse L.L.C. | System and methods for matching an utterance to a template hierarchy |
US20110288868A1 (en) * | 2010-05-19 | 2011-11-24 | Lloyd Matthew I | Disambiguation of contact information using historical data |
US9177045B2 (en) * | 2010-06-02 | 2015-11-03 | Microsoft Technology Licensing, Llc | Topical search engines and query context models |
US20110301955A1 (en) * | 2010-06-07 | 2011-12-08 | Google Inc. | Predicting and Learning Carrier Phrases for Speech Input |
US20110314012A1 (en) * | 2010-06-16 | 2011-12-22 | Microsoft Corporation | Determining query intent |
US20110314390A1 (en) * | 2010-06-18 | 2011-12-22 | Microsoft Corporation | Techniques to dynamically modify themes based on messaging |
US20120016873A1 (en) * | 2010-07-16 | 2012-01-19 | Michael Mathieson | Method and system for ranking search results based on categories |
US20140149399A1 (en) * | 2010-07-22 | 2014-05-29 | Google Inc. | Determining user intent from query patterns |
US8521526B1 (en) * | 2010-07-28 | 2013-08-27 | Google Inc. | Disambiguation of a spoken query term |
US20120053927A1 (en) * | 2010-09-01 | 2012-03-01 | Microsoft Corporation | Identifying topically-related phrases in a browsing sequence |
US20120078919A1 (en) * | 2010-09-29 | 2012-03-29 | Fujitsu Limited | Comparison of character strings |
US20120084291A1 (en) * | 2010-09-30 | 2012-04-05 | Microsoft Corporation | Applying search queries to content sets |
US20120096033A1 (en) * | 2010-10-14 | 2012-04-19 | Microsoft Corporation | Disambiguation of Entities |
US20130212475A1 (en) * | 2010-11-01 | 2013-08-15 | Koninklijke Philips Electronics N.V. | Suggesting relevant terms during text entry |
US20120158703A1 (en) * | 2010-12-16 | 2012-06-21 | Microsoft Corporation | Search lexicon expansion |
US20120158693A1 (en) * | 2010-12-17 | 2012-06-21 | Yahoo! Inc. | Method and system for generating web pages for topics unassociated with a dominant url |
US8560321B1 (en) * | 2011-01-05 | 2013-10-15 | Interactions Corportion | Automated speech recognition system for natural language understanding |
US20120290293A1 (en) * | 2011-05-13 | 2012-11-15 | Microsoft Corporation | Exploiting Query Click Logs for Domain Detection in Spoken Language Understanding |
Cited By (13)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US9620147B2 (en) | 2012-06-19 | 2017-04-11 | International Business Machines Corporation | Intent discovery in audio or text-based conversation |
US8983840B2 (en) * | 2012-06-19 | 2015-03-17 | International Business Machines Corporation | Intent discovery in audio or text-based conversation |
US20180203833A1 (en) * | 2016-11-04 | 2018-07-19 | Semantic Machines, Inc. | Data collection for a new conversational dialogue system |
US10824798B2 (en) * | 2016-11-04 | 2020-11-03 | Semantic Machines, Inc. | Data collection for a new conversational dialogue system |
US10713288B2 (en) | 2017-02-08 | 2020-07-14 | Semantic Machines, Inc. | Natural language content generator |
US10762892B2 (en) | 2017-02-23 | 2020-09-01 | Semantic Machines, Inc. | Rapid deployment of dialogue system |
US10586530B2 (en) | 2017-02-23 | 2020-03-10 | Semantic Machines, Inc. | Expandable dialogue system |
US11069340B2 (en) | 2017-02-23 | 2021-07-20 | Microsoft Technology Licensing, Llc | Flexible and expandable dialogue system |
US11132499B2 (en) | 2017-08-28 | 2021-09-28 | Microsoft Technology Licensing, Llc | Robust expandable dialogue system |
US11182565B2 (en) * | 2018-02-23 | 2021-11-23 | Samsung Electronics Co., Ltd. | Method to learn personalized intents |
US11314940B2 (en) | 2018-05-22 | 2022-04-26 | Samsung Electronics Co., Ltd. | Cross domain personalized vocabulary learning in intelligent assistants |
US20200142719A1 (en) * | 2018-11-02 | 2020-05-07 | International Business Machines Corporation | Automatic generation of chatbot meta communication |
CN113657120A (en) * | 2021-08-23 | 2021-11-16 | 深圳科卫机器人科技有限公司 | Human-computer interaction intention analysis method and device, computer equipment and storage medium |
Also Published As
Publication number | Publication date |
---|---|
US8688453B1 (en) | 2014-04-01 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US8688453B1 (en) | Intent mining via analysis of utterances | |
US8812299B1 (en) | Class-based language model and use | |
US8606581B1 (en) | Multi-pass speech recognition | |
US8914277B1 (en) | Speech and language translation of an utterance | |
US11093707B2 (en) | Adversarial training data augmentation data for text classifiers | |
US9311913B2 (en) | Accuracy of text-to-speech synthesis | |
CN112115706B (en) | Text processing method and device, electronic equipment and medium | |
CN106406806B (en) | Control method and device for intelligent equipment | |
US10740380B2 (en) | Incremental discovery of salient topics during customer interaction | |
US20200184955A1 (en) | Image-based approaches to identifying the source of audio data | |
US11189269B2 (en) | Adversarial training data augmentation for generating related responses | |
CN108428446A (en) | Audio recognition method and device | |
CN111428010B (en) | Man-machine intelligent question-answering method and device | |
US10783879B2 (en) | System and method for rule based modifications to variable slots based on context | |
CN111145733B (en) | Speech recognition method, speech recognition device, computer equipment and computer readable storage medium | |
EP2801092A1 (en) | Methods, apparatuses and computer program products for implementing automatic speech recognition and sentiment detection on a device | |
CN108986790A (en) | The method and apparatus of voice recognition of contact | |
CN112530408A (en) | Method, apparatus, electronic device, and medium for recognizing speech | |
US9984687B2 (en) | Image display device, method for driving the same, and computer readable recording medium | |
US10607601B2 (en) | Speech recognition by selecting and refining hot words | |
CN111949255A (en) | Script compiling method, device, equipment and storage medium based on voice | |
US11625630B2 (en) | Identifying intent in dialog data through variant assessment | |
KR20170010978A (en) | Method and apparatus for preventing voice phishing using pattern analysis of communication content | |
KR101801250B1 (en) | Method and system for automatically tagging themes suited for songs | |
CN112447173A (en) | Voice interaction method and device and computer storage medium |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
AS | Assignment |
Owner name: NUANCE COMMUNICATIONS, INC., MASSACHUSETTS Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:JOSHI, SACHINDRA;GODBOLE, SHANTANU;REEL/FRAME:032258/0913 Effective date: 20110211 |
|
STCB | Information on status: application discontinuation |
Free format text: ABANDONED -- FAILURE TO RESPOND TO AN OFFICE ACTION |