Abstract: A system and method for providing an easy-to-use interface for verifying semantic tags in a steering application in order to generate a natural language grammar. The method includes obtaining user responses to open-ended steering questions, automatically grouping the user responses into groups based on their semantic meaning, and automatically assigning preliminary semantic tags to each of the groups. The user interface enables the user to validate the content of the groups to ensure that all responses within a group have the same semantic meaning and to add or edit semantic tags associated with the groups. The system and method may be applied to interactive voice response (IVR) systems, as well as customer service systems that can communicate with a user via a text or written interface.

Abstract: Inferring a natural language grammar is based on providing natural language understanding (NLU) data with concept annotations according to an application ontology characterizing a relationship structure between application-related concepts for a given NLU application. An application grammar is then inferred from the concept annotations and the application ontology.

Abstract: A system and method for providing an easy-to-use interface for verifying semantic tags in a steering application in order to generate a natural language grammar. The method includes obtaining user responses to open-ended steering questions, automatically grouping the user responses into groups based on their semantic meaning, and automatically assigning preliminary semantic tags to each of the groups. The user interface enables the user to validate the content of the groups to ensure that all responses within a group have the same semantic meaning and to add or edit semantic tags associated with the groups. The system and method may be applied to interactive voice response (IVR) systems, as well as customer service systems that can communicate with a user via a text or written interface.

Abstract: Multiple natural language understanding (NLU) interpretation selection models may be generated. The NLU interpretation selection models may include a generic NLU interpretation selection model that is not specialized for a specific set of NLU interpretations type and one or more specialized NLU interpretation selection models, each of which may be specific to a particular set of NLU interpretations type. The specialized NLU interpretation selection model(s) may be utilized to process natural language input data comprising data corresponding to their respective sets of NLU interpretations type(s). The generic NLU interpretation selection model may be utilized to process natural language input data comprising data that does not correspond to the sets of NLU interpretations type(s) associated with the specialized NLU interpretation selection model(s).

Abstract: An ontology stores information about a domain of an automatic speech recognition (ASR) application program. The ontology is augmented with information that enables subsequent automatic generation of a speech understanding grammar for use by the ASR application program. The information includes hints about how a human might talk about objects in the domain, such as preludes (phrases that introduce an identification of the object) and postludes (phrases that follow an identification of the object).

Abstract: In FAQ based systems, associating questions with answers can be a time consuming task if performed manually. In one embodiment, a method of building a frequently-asked questions (FAQ) portal can include creating cluster labels. The labels can include predefined universal semantic labels and application-specific labels. The method can further include applying the cluster labels to clusters of queries within an FAQ application. The method can additionally include adjusting the application-specific labels to support combined and newly created clusters of queries based on application-specific queries within the FAQ application on an ongoing basis and reapplying the universal semantic labels and the adjusted application-specific labels to the combined and newly created clusters of queries. The method and system proposed herein allow for the automated clustering of queries and association with applicable answers, which leads to higher efficiencies for a faster response time for a user.

Abstract: A human-machine dialogue system is described which has multiple computer-implemented dialogue components. A user client delivers output prompts to a human user and receives dialogue inputs from the human user including speech inputs. An automatic speech recognition (ASR) engine processes the speech inputs to determine corresponding sequences of representative text words. A natural language understanding (NLU) engine processes the text words to determine corresponding NLU-ranked semantic interpretations. A semantic re-ranking module re-ranks the NLU-ranked semantic interpretations based on at least one of dialogue context information and world knowledge information. A dialogue manager responds to the re-ranked semantic interpretations and generates the output prompts so as to manage a dialogue process with the human user.

Abstract: An automated method is described for developing an automated speech input semantic classification system such as a call routing system. A set of semantic classifications is defined for classification of input speech utterances, where each semantic classification represents a specific semantic classification of the speech input. The semantic classification system is trained from training data having little or no in-domain manually transcribed training data, and then operated to assign input speech utterances to the defined semantic classifications. Adaptation training data based on input speech utterances is collected with manually assigned semantic labels. When the adaptation training data satisfies a pre-determined adaptation criteria, the semantic classification system is automatically retrained based on the adaptation training data.

Abstract: Techniques for grouping a plurality of samples automatically transcribed from a plurality of utterances. The method comprises forming clusters from the plurality of samples, wherein the clusters include two or more of the plurality of samples. One or more samples are selected from a cluster and manually-processed data samples for the one or more samples are obtained. A weighting factor may be assigned to the data samples based, at least in part, on the number of samples in the cluster associated with the selected data sample.

Abstract: A computer-implemented method is described for developing query tags for classification of user queries to a call routing application. Multiple user query corpuses are accessed that contain user queries from multiple call routing applications in multiple different vertical domains. A set of frequent user queries are selected which appear in multiple different query corpuses in multiple different vertical domains. From these are developed frequent query tags for semantic classification of the frequent user queries. The frequent query tags are then stored in a query tag database.

Abstract: Operation of an automated dialog system is described using a source language to conduct a real time human machine dialog process with a human user using a target language. A user query in the target language is received and automatically machine translated into the source language. An automated reply of the dialog process is then delivered to the user in the target language. If the dialog process reaches an initial assistance state, a first human agent using the source language is provided to interact in real time with the user in the target language by machine translation to continue the dialog process. Then if the dialog process reaches a further assistance state, a second human agent using the target language is provided to interact in real time with the user in the target language to continue the dialog process.

Abstract: Techniques for grouping a plurality of samples automatically transcribed from a plurality of utterances. The method comprises forming clusters from the plurality of samples, wherein the clusters include two or more of the plurality of samples. One or more samples are selected from a cluster and manually-processed data samples for the one or more samples are obtained. A weighting factor may be assigned to the data samples based, at least in part, on the number of samples in the cluster associated with the selected data sample.

Abstract: A method for developing a voice user interface for a statistical semantic system is described. A set of semantic meanings is defined that reflect semantic classification of a user input dialog. Then, a set of speech dialog prompts is automatically developed from an annotated transcription corpus for directing user inputs to corresponding final semantic meanings. The statistical semantic system may be a call routing application where the semantic meanings are call routing destinations.

Abstract: A method for developing a voice user interface for a statistical semantic system is described. A set of semantic meanings is defined that reflect semantic classification of a user input dialog. Then, a set of speech dialog prompts is automatically developed from an annotated transcription corpus for directing user inputs to corresponding final semantic meanings. The statistical semantic system may be a call routing application where the semantic meanings are call routing destinations.

Abstract: An automated method is described for developing an automated speech input semantic classification system such as a call routing system. A set of semantic classifications is defined for classification of input speech utterances, where each semantic classification represents a specific semantic classification of the speech input. The semantic classification system is trained from training data having little or no in-domain manually transcribed training data, and then operated to assign input speech utterances to the defined semantic classifications. Adaptation training data based on input speech utterances is collected with manually assigned semantic labels. When the adaptation training data satisfies a pre-determined adaptation criteria, the semantic classification system is automatically retrained based on the adaptation training data.

Abstract: A computerized method is provided for electronically directing a call to a class, such that an utterance spoken by a speaker and received by a call-routing system is classified by the call-routing system as being associated with the class, such that the call-routing system includes a speech-recognition module, a feature-extraction module, and a classification module. The method includes extracting features from recognized speech; weighting elements of a feature vector with respective speech-recognition scores, wherein each weighting element is associated with one of the features; ranking classes to which the features are associated; and electronically directing the call to a highest-ranking class.

Abstract: Systems and methods consistent with the present invention determine whether to accept one of a plurality of intermediate recognition results output by a speech recognition system as a final recognition result. The system first combines a plurality of speech rejection features into a feature function in which weights are assigned to each rejection feature in accordance with a recognition accuracy of each rejection feature. Feature values are then calculated for each of the rejection features using the plurality of intermediate recognition results. The system next computes the feature function according to the calculated feature values to determine a rejection decision value. Finally, one of the plurality of intermediate recognition results is accepted as the final recognition result according to the rejection decision value.

Abstract: Electromagnetic wave concentrator, especially a solar energy concentrator, in the form of a thin plate made up of a succession of identical light coupling modules each made up of two identical prisms made of transparent materials and having different indexes of refraction. The physical characteristics of the prisms are selected to cause continuous bending of the light beams, striking an incident face of the concentrator, toward a terminal edge surface of the concentrator plate.