2142 results about "Utterance" patented technology

A system and method for processing multi-modal device interactions in a natural language voice services environment may be provided. In particular, one or more multi-modal device interactions may be received in a natural language voice services environment that includes one or more electronic devices. The multi-modal device interactions may include a non-voice interaction with at least one of the electronic devices or an application associated therewith, and may further include a natural language utterance relating to the non-voice interaction. Context relating to the non-voice interaction and the natural language utterance may be extracted and combined to determine an intent of the multi-modal device interaction, and a request may then be routed to one or more of the electronic devices based on the determined intent of the multi-modal device interaction.

The system and method described herein may provide a natural language content dedication service in a voice services environment. In particular, providing the natural language content dedication service may generally include detecting multi-modal device interactions that include requests to dedicate content, identifying the content requested for dedication from natural language utterances included in the multi-modal device interactions, processing transactions for the content requested for dedication, processing natural language to customize the content for recipients of the dedications, and delivering the customized content to the recipients of the dedications.

A cooperative conversational voice user interface is provided. The cooperative conversational voice user interface may build upon short-term and long-term shared knowledge to generate one or more explicit and/or implicit hypotheses about an intent of a user utterance. The hypotheses may be ranked based on varying degrees of certainty, and an adaptive response may be generated for the user. Responses may be worded based on the degrees of certainty and to frame an appropriate domain for a subsequent utterance. In one implementation, misrecognitions may be tolerated, and conversational course may be corrected based on subsequent utterances and/or responses.

An iterative process is provided for interacting with a voice information service. Such a service may permit, for example, a user to search one or more databases and may provide one or more search results to the user. Such a service may be suitable, for example, for searching for a desired entity or object within the database(s) using speech as an input and navigational tool. Applications of such a service may include, for instance, speech-enabled searching services such as a directory assistance service or any other service or application involving a search of information. In one example implementation, an automatic speech recognition (ASR) system is provided that performs a speech recognition and database search in an iterative fashion. With each iteration, feedback may be provided to the user presenting potentially relevant results. In one specific ASR system, a user desiring to locate information relating to a particular entity or object provides an utterance to the ASR. Upon receiving the utterance, the ASR determines a recognition set of potentially relevant search results related to the utterance and presents to the user recognition set information in an interface of the ASR. The recognition set information includes, for instance, reference information stored internally at the ASR for a plurality of potentially relevant recognition results. The recognition set information may be used as input to the ASR providing a feedback mechanism. In one example implementation, the recognition set information may be used to determine a restricted grammar for performing a further recognition.

A system and method are provided for receiving speech and/or non-speech communications of natural language questions and/or commands and executing the questions and/or commands. The invention provides a conversational human-machine interface that includes a conversational speech analyzer, a general cognitive model, an environmental model, and a personalized cognitive model to determine context, domain knowledge, and invoke prior information to interpret a spoken utterance or a received non-spoken message. The system and method creates, stores and uses extensive personal profile information for each user, thereby improving the reliability of determining the context of the speech or non-speech communication and presenting the expected results for a particular question or command.

A computer is used to perform recorded actions. The computer receives recorded spoken utterances of actions. The computer then performs speech recognition on the recorded spoken utterances to generate texts of the actions. The computer then parses the texts to determine properties of the actions. After parsing the texts, permits the user to indicate that the user has reviewed one or more actions. The computer then automatically carries out the actions indicated as having been reviewed by the user.

A speaker recognition system for authenticating a mobile device user includes an enrollment and learning software module, a voice biometric authentication software module, and a secure software application. Upon request by a user of the mobile device, the enrollment and learning software module displays text prompts to the user, receives speech utterances from the user, and produces a voice biometric print. The enrollment and training software module determines when a voice biometric print has met at least a quality threshold before storing it on the mobile device. The secure software application prompts a user requiring authentication to repeat an utterance based at least on an attribute of a selected voice biometric print, receives a corresponding utterance, requests the voice biometric authentication software module to verify the identity of the second user using the utterance, and, if the user is authenticated, imports the voice biometric print.

A speech translation system and methods for cross-lingual communication that enable users to improve and modify content and usage of the system and easily abort or reset translation. The system includes a speech recognition module configured for accepting an utterance, a machine translation module, an interface configured to communicate the utterance and proposed translation, a correction module and an abort action unit that removes any hypotheses or partial hypotheses and terminates translation. The system also includes modules for storing favorites, changing language mode, automatically identifying language, providing language drills, viewing third party information relevant to conversation, among other things.

A real-time system incorporating speech recognition and linguistic processing for recognizing a spoken query by a user and distributed between client and server, is disclosed. The system accepts user's queries in the form of speech at the client where minimal processing extracts a sufficient number of acoustic speech vectors representing the utterance. These vectors are sent via a communications channel to the server where additional acoustic vectors are derived. Using Hidden Markov Models (HMMs), and appropriate grammars and dictionaries conditioned by the selections made by the user, the speech representing the user's query is fully decoded into text (or some other suitable form) at the server. This text corresponding to the user's query is then simultaneously sent to a natural language engine and a database processor where optimized SQL statements are constructed for a full-text search from a database for a recordset of several stored questions that best matches the user's query. Further processing in the natural language engine narrows the search to a single stored question. The answer corresponding to this single stored question is next retrieved from the file path and sent to the client in compressed form. At the client, the answer to the user's query is articulated to the user using a text-to-speech engine in his or her native natural language. The system requires no training and can operate in several natural languages.

Systems and methods are provided for receiving speech and non-speech communications of natural language questions and/or commands, transcribing the speech and non-speech communications to textual messages, and executing the questions and/or commands. The invention applies context, prior information, domain knowledge, and user specific profile data to achieve a natural enviromnent for one or more users presenting questions or commands across multiple domains. The systems and methods creates, stores and uses extensive personal profile information for each user, thereby improving the reliability of determining the context of the speech and non-speech communications and presenting the expected results for a particular question or command.

The invention relates to a method and apparatus for training a multilingual speech model set. The multilingual speech model set generated is suitable for use by a speech recognition system for recognizing spoken utterances for at least two different languages. The invention allows using a single speech recognition unit with a single speech model set to perform speech recognition on utterances from two or more languages. The method and apparatus make use of a group of a group of acoustic sub-word units comprised of a first subgroup of acoustic sub-word units associated to a first language and a second subgroup of acoustic sub-word units associated to a second language where the first subgroup and the second subgroup share at least one common acoustic sub-word unit. The method and apparatus also make use of a plurality of letter to acoustic sub-word unit rules sets, each letter to acoustic sub-word unit rules set being associated to a different language. A set of untrained speech models is trained on the basis of a training set comprising speech tokens and their associated labels in combination with the group of acoustic sub-word units and the plurality of letter to acoustic sub-word unit rules sets. The invention also provides a computer readable storage medium comprising a program element for implementing the method for training a multilingual speech model set.

Methods, apparatus, and products are disclosed for supporting multi-lingual user interaction with a multimodal application, the application including a plurality of VoiceXML dialogs, each dialog characterized by a particular language, supporting multi-lingual user interaction implemented with a plurality of speech engines, each speech engine having a grammar and characterized by a language corresponding to one of the dialogs, with the application operating on a multimodal device supporting multiple modes of interaction including a voice mode and one or more non-voice modes, the application operatively coupled to the speech engines through a VoiceXML interpreter, the VoiceXML interpreter: receiving a voice utterance from a user; determining in parallel, using the speech engines, recognition results for each dialog in dependence upon the voice utterance and the grammar for each speech engine; administering the recognition results for the dialogs; and selecting a language for user interaction in dependence upon the administered recognition results.

An adaptive speech recognition system is provided including an input for receiving a signal derived from a spoken utterance indicative of a certain vocabulary item, a speech recognition dictionary, a speech recognition unit and an adaptation module. The speech recognition dictionary has a plurality of vocabulary items each being associated to a respective dictionary transcription group. The speech recognition unit is in an operative relationship with the speech recognition dictionary and selects a certain vocabulary item from the speech recognition dictionary as being a likely match to the signal received at the input. The results of the speech recognition process are provided to the adaptation module. The adaptation module includes a transcriptions bank having a plurality of orthographic groups, each including a plurality of transcriptions associated with a common vocabulary item. A transcription selector module in the adaptation module retrieves a given orthographic group from the transcriptions bank on a basis of the vocabulary item recognized by the speech recognition unit. The transcription selector module processes the given orthographic group on the basis of the signal received at the input to select a certain transcription from the transcriptions bank. The adaptation module then modifies a dictionary transcription group corresponding to the vocabulary item selected as being a likely match to the signal received at the input on the basis of the selected certain transcription.

Ordering recognition results produced by an automatic speech recognition (‘ASR’) engine for a multimodal application implemented with a grammar of the multimodal application in the ASR engine, with the multimodal application operating in a multimodal browser on a multimodal device supporting multiple modes of interaction including a voice mode and one or more non-voice modes, the multimodal application operatively coupled to the ASR engine through a VoiceXML interpreter, includes: receiving, in the VoiceXML interpreter from the multimodal application, a voice utterance; determining, by the VoiceXML interpreter using the ASR engine, a plurality of recognition results in dependence upon the voice utterance and the grammar; determining, by the VoiceXML interpreter according to semantic interpretation scripts of the grammar, a weight for each recognition result; and sorting, by the VoiceXML interpreter, the plurality of recognition results in dependence upon the weight for each recognition result.

Improved techniques are disclosed for permitting a user to employ more human-based grammar (i.e., free form or conversational input) while addressing a target system via a voice system. For example, a technique for determining intent associated with a spoken utterance of a user comprises the following steps/operations. Decoded speech uttered by the user is obtained. An intent is then extracted from the decoded speech uttered by the user. The intent is extracted in an iterative manner such that a first class is determined after a first iteration and a sub-class of the first class is determined after a second iteration. The first class and the sub-class of the first class are hierarchically indicative of the intent of the user, e.g., a target and data that may be associated with the target. The multi-stage intent extraction approach may have more than two iterations. By way of example only, the user intent extracting step may further determine a sub-class of the sub-class of the first class after a third iteration, such that the first class, the sub-class of the first class, and the sub-class of the sub-class of the first class are hierarchically indicative of the intent of the user.

Interpretation from a first language to a second language via one or more communication devices is performed through a communication network (e.g. phone network or the internet) using a server for performing recognition and interpretation tasks, comprising the steps of: receiving an input speech utterance in a first language on a first mobile communication device; conditioning said input speech utterance; first transmitting said conditioned input speech utterance to a server; recognizing said first transmitted speech utterance to generate one or more recognition results; interpreting said recognition results to generate one or more interpretation results in an interlingua; mapping the interlingua to a second language in a first selected format; second transmitting said interpretation results in the first selected format to a second mobile communication device; and presenting said interpretation results in a second selected format on said second communication device.