Nuance Communications, Inc.

Réal Tremblay

Stephen Douglas Peters

Simona Gandrabur

Nicolae Duta

Serge Robillard

Jerome Tremblay

S Douglas Peters

Jean-Francois Lavallée

Jean-Michel Attendu

Andrew D Mauro

Jean-François Lavallée

A system and method incorporate prior knowledge into the optimization and regularization of a classification and regression model. The optimization may be a regularization process and the prior knowledge may be incorporated through adjustment of a cost function. A method of at least one processor developing a classification and regression model may be provided. The method may be implemented by at least one processor that implements classification and regression model functionality, including receiving training data and adjusting the model according to the training data; testing the classification and regression model; and employing prior knowledge during an optimization of the classification and regression model. The regularizing can include adjusting feature weights according to prior knowledge. In various embodiments, such systems and methods can be used in the processing of language inputs, e.g., speech and/or text inputs, to achieve greater interpretation accuracy.

System and method for language processing using adaptive regularization

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).

Auto-generation of parsing grammars from a concept ontology

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.

Ontology and annotation driven grammar inference

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 from training data substantially without manually transcribed in-domain 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 from at least one source of already collected language data. When the adaptation training data satisfies a pre-determined adaptation criteria, the semantic classification system is automatically retrained based on the adaptation training data.

Speech recognition semantic classification training

A human-machine dialog system is described which has multiple computer-implemented dialog components. A user client delivers output prompts to a human user and receives dialog 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 dialog context information and world knowledge information. A dialog manager responds to the re-ranked semantic interpretations and generates the output prompts so as to manage a dialog process with the human user.

Semantic re-ranking of NLU results in conversational dialogue applications

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.

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Outremont, Canada

Réal Tremblay