Nortel Networks Corporation

Nuance Communications, Inc.

Stephen Douglas Peters

Daniel Boies

Elizabeth Ann Dykstra-Erickson

Aimee Piercy

David Andrew Mauro

Susan Dawnstarr Daniel

Réal Tremblay

Henri Bouvier

Benoit Dumoulin

Paweena Attayadmawittaya

Andrew Jonathan Watson

Serge Robillard

Jerome Tremblay

Matthieu Hebert

Matthieu H&eacute;bert

A method of providing a task assistant to provide an interface to an application is described. The method comprises receiving input from a user through multimodal input including a plurality of speech input, typing input, and touch input, interpreting the input, and providing a formatted query to the application, receiving data from the application in response to the query, and providing a response to the user through multimodal output including a plurality of: speech output, text output, non-speech audio output, haptic output, and visual non-text output.

Task assistant

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

A speech recognition system having an input for receiving an input signal indicative of a spoken utterance that is indicative of at least one speech element. The system further includes a first processing unit operative for processing the input signal to derive from a speech recognition dictionary a speech model associated to a given speech element that constitutes a potential match to the at least one speech element. The system further comprised a second processing unit for generating a modified version of the speech model on the basis of the input signal. The system further provides a third processing unit for processing the input signal on the basis of the modified version of the speech model to generate a recognition result indicative of whether the modified version of the at least one speech model constitutes a match to the input signal. The second processing unit allows the speech model to be modified on the basis of the recognition attempt thereby allowing speech recognition to be effected on the basis of the modified speech model. This permits adaptation of the speech models during the recognition process. The invention further provides an apparatus, method and computer readable medium for implementing the second processing unit.

Method and apparatus providing hypothesis driven speech modelling for use in speech recognition

An apparatus for normalizing speech feature elements in a signal derived from a spoken utterance. The apparatus includes an input, a processing unit and an output. The input receives speech feature elements transmitted over a channel that induces a channel specific distortion in the speech feature elements. The processing unit is coupled to the input and is operative for altering the speech feature elements to generate normalized speech feature elements. The normalized speech feature elements simulate a transmission of the speech feature elements over a reference channel that is other than the channel over which the transmission actually takes place. The apparatus can be used as a speech recognition pre-processing unit to reduce channel related variability in the signal on which speech recognition is to be performed.

Method and apparatus for normalizing channel specific speech feature elements

A method and apparatus for generating a pair of data elements is provided suitable for use in a speaker verification system. The pair includes a first element representative of a speaker independent template and a second element representative of an extended speaker specific speech pattern. An audio signal forming enrollment data associated with a given speaker is received and processed to derive a speaker independent template and a speaker specific speech pattern. The speaker specific speech pattern is then processed to derive an extended speaker specific speech pattern. The extended speaker specific speech pattern includes a set of expanded speech models, each expanded speech model including a plurality of groups of states, the groups of states being linked to one another by inter-group transitions. Optionally, the expanded speech models are processed on the basis of the enrollment data to condition at least one of the plurality of inter-group transitions.

Method and apparatus for hierarchical training of speech models for use in speaker verification

A method and apparatus for use in the field of speaker verification. The invention provides a method and apparatus for generating a pair of data elements, namely a first element representative of a speaker specific speech pattern and a second element representative of a biased normalizing template, the pair of data elements being suitable for use in a speaker verification system. The invention provides receiving an audio signal representative of a training token associated with a given speaker and processing the training token on a basis of a reference speaker independent model set to derive a speaker independent normalizing template. The invention further provides processing the training token on a basis of a reference speaker independent model set for generating a speaker specific speech pattern. The speaker specific speech pattern and the speaker independent normalizing template are then processed to derive a biased normalizing template. The pair of data elements is then in a format suitable for use by a speaker verification system. The invention further provides a computer readable medium containing a program element suitable for generating a pair of data elements suitable for use in a speaker verification system.

Method and apparatus for use in speaker verification

The present invention provides improved foreground-speech signal endpointing by computing a spectral stationarity statistic. This statistic is used by a finite state machine to endpoint speech. Endpointing using the spectral stationarity statistic is less susceptible to background noise than endpointing using conventional measures. The present invention uses frame-synchronous quantile estimation to generate a mask signal for signal to Noise Ratio Normalization.

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Pointe-Claire, Canada

Stephen Douglas Peters