Gm Global Technolgoy Operations, Inc.

Lucent Technologies Inc.

At&t Intellectual Property I, LP

Rathinavelu Chengalvarayan

Gaurav Talwar

Timothy Grost

John J Correia

Jason W Clark

Scott M Pennock

Dana Brian Fecher

Michael A Wuergler

Sherri J Voran-Nowak

Xufang Zhao

David Lynn Thomson

Edward J Abeska

Jesse T Gratke

Uma Arun

Jeremy M Spaulding

Christopher L Oesterling

Eray Yasan

Jeffrey Stefan

Kevin R Krause

David M George

Lawrence Daniel Cepuran

Steven J Ross

Anand Rangaswamy Setlur

Richard H Ketchum

Subhash B Gullapalli

Systems, methods, and computer-readable storage devices are for identifying a user profile for speech recognition. The user profile is selected from one of several user profiles which are all associated with a speaker, and can be selected based on the identity of the speaker, the location of the speaker, the device the speaker is using, or other relevant parameters. Such parameters can be hierarchical, having multiple layers, and can also be dependent or independent from one another. Using the parameters identified, the user profile is selected and used to recognize speech.

System and method for personalization in speech recognition

System and method for personalization in speech recogniton

A method of and system for speech synthesis. First and second text inputs are received in a text-to-speech system, and processed into respective first and second speech outputs corresponding to stored speech respectively from first and second speakers using a processor of the system. The second speech output of the second speaker is adapted to sound like the first speech output of the first speaker.

Speech adaptation in speech synthesis

A speech recognition method includes receiving a nametag utterance, decoding the nametag utterance to recognize constituent subwords of the nametag utterance, determining the number of subwords in the nametag utterance, and associating the nametag utterance with one or more of a plurality of different nametag clusters based on the number of subwords in the nametag utterance. According to preferred aspects of the method, a confusability check is performed on the nametag utterance within the cluster(s) associated with the nametag utterance, stored nametags are received from memory by decoding the nametag utterance within the cluster(s) associated with the nametag utterance, and the stored nametags are played back by cluster.

Dynamic clustering of nametags in an automated speech recognition system

A dialing mode of a telematics unit in a vehicle is controlled by monitoring for dialing digits from a vehicle occupant, determining whether the type of dialing digits are continuous dialing digits or discrete dialing digits, establishing a continuous mode for receiving continuous dialing digits or a discrete mode for receiving discrete dialing digits based on the determination, and if the type of dialing digits changes, switching the established mode.

Method of controlling dialing modes in a vehicle

A system and method for providing speech recognition functionality offers improved accuracy and robustness in noisy environments having multiple speakers. The described technique includes receiving speech energy and converting the received speech energy to a digitized form. The digitized speech energy is decomposed into features that are then projected into a feature space having multiple speaker subspaces. The projected features fall either into one of the multiple speaker subspaces or outside of all speaker subspaces. A speech recognition operation is performed on a selected one of the multiple speaker subspaces to resolve the utterance to a command or data.

Singular value decomposition for improved voice recognition in presence of multi-talker background noise

A method and system of speech synthesis. A text input is received in a text-to-speech system and, using a processor of the system, the text input is processed into synthesized speech which is established as unintelligible. The text input is reprocessed into subsequent synthesized speech and output to a user via a loudspeaker to correct the unintelligible synthesized speech. In one embodiment, the synthesized speech can be established as unintelligible by predicting intelligibility of the synthesized speech, and determining that the predicted intelligibility is lower than a minimum threshold. In another embodiment, the synthesized speech can be established as unintelligible by outputting the synthesized speech to the user via the loudspeaker, and receiving an indication from the user that the synthesized speech is not intelligible.

Correcting unintelligible synthesized speech

Methods of automatic speech recognition for premature enunciation. In one method, a) a user is prompted to input speech, then b) a listening period is initiated to monitor audio via a microphone, such that there is no pause between the end of step a) and the beginning of step b), and then the begin-speaking audible indicator is communicated to the user during the listening period. In another method, a) at least one audio file is played including both a prompt for a user to input speech and a begin-speaking audible indicator to the user, b) a microphone is activated to monitor audio, after playing the prompt but before playing the begin-speaking audible indicator in step a), and c) speech is received from the user via the microphone.

Speech recognition for premature enunciation

A method of generating proxy acoustic models for use in automatic speech recognition includes training acoustic models from speech received via microphone from male speakers of a first language, and adapting the acoustic models in response to language-independent speech data from female speakers of a second language, to generate proxy acoustic models for use during runtime of speech recognition of an utterance from a female speaker of the first language.

Male acoustic model adaptation based on language-independent female speech data

A speech recognition method includes receiving input speech from a user, processing the input speech using a first grammar to obtain parameter values of a first N-best list of vocabulary, comparing a parameter value of a top result of the first N-best list to a threshold value, and if the compared parameter value is below the threshold value, then additionally processing the input speech using a second grammar to obtain parameter values of a second N-best list of vocabulary. Other preferred steps include: determining the input speech to be in-vocabulary if any of the results of the first N-best list is also present within the second N-best list, but out-of-vocabulary if none of the results of the first N-best list is within the second N-best list; and providing audible feedback to the user if the input speech is determined to be out-of-vocabulary.

Distinguishing out-of-vocabulary speech from in-vocabulary speech

A method for dynamic nametag scoring includes receiving at least one confusion table including at least one circumstantial condition wherein the confusion table is based on a plurality of phonetically balanced utterances, determining a plurality of templates for the nametag based on the received confusion tables, and determining a global nametag score for the nametag based on the determined templates. A computer usable medium with suitable computer program code is employed for dynamic nametag scoring.

Method and system for dynamic nametag scoring

A speech recognition method includes the steps of receiving input speech containing vocabulary, processing the input speech with a grammar to obtain N-best hypotheses and associated parameter values, and determining whether a first-best hypothesis of the N-best hypotheses is confusable with any vocabulary within the grammar. The first-best hypothesis is accepted as recognized speech corresponding to the received input speech if the first-best hypothesis is not determined to be confusable with any vocabulary within the grammar. Where the first-best hypothesis is determined to be confusable, at least one parameter value of the first-best hypothesis can be compared to at least one threshold value, and accepting the second-best as the recognized speech, if its confidence score is within certain lower and upper threshold values and is not confusable with the first-best. The first-best hypothesis can be accepted as recognized speech corresponding to the received input speech, if the parameter value of the first-best hypothesis is greater than the threshold value.

Correcting substitution errors during automatic speech recognition by accepting a second best when first best is confusable

A system and method for tuning a speech recognition engine to an individual microphone using a database containing acoustical models for a plurality of microphones. Microphone performance characteristics are obtained from a microphone at a speech recognition engine, the database is searched for an acoustical model that matches the characteristics, and the speech recognition engine is then modified based on the matching acoustical model.

Growing community of inventors

Naperville, IL, United States of America

Rathinavelu Chengalvarayan