International Business Machines Corporation

Nuance Communications, Inc.

Lidia Mangu

Samuel Jonathan Thomas

Hong-Kwang Jeff Kuo

Hagen Soltau

Brian E D Kingsbury

Bhuvana Ramabhadran

Vaibhava Goel

Etienne Marcheret

Stanley S Chen

Abhinav Sethy

Kenneth W Church

Laurence P Sansone

Fadi Biadsy

Michael Alan Picheny

George Andrei Saon

Sabine V Deligne

Mukund Padmanabhan

Methods and systems for automatic speech recognition and methods and systems for training acoustic language models are disclosed. In accordance with one automatic speech recognition method, an acoustic input data set is analyzed to identify portions of the input data set that conform to a general language and to identify portions of the input data set that conform to at least one dialect of the general language. In addition, a general language model and at least one dialect language model is applied to the input data set to perform speech recognition by dynamically selecting between the models in accordance with each of the identified portions. Further, speech recognition results obtained in accordance with the application of the models is output.

Dialect-specific acoustic language modeling and speech recognition

A method of combining data streams from fixed audio-visual sensors with data streams from personal mobile devices including, forming a communication link with at least one of one or more personal mobile devices; receiving at least one of an audio data stream and/or a video data stream from the at least one of the one or more personal mobile devices; determining the quality of the at least one of the audio data stream and/or the video data stream, wherein the audio data stream and/or the video data stream having a quality above a threshold quality is retained; and combining the retained audio data stream and/or the video data stream with the data streams from the fixed audio-visual sensors.

Combining installed audio-visual sensors with ad-hoc mobile audio-visual sensors for smart meeting rooms

A method, executed by a computer, includes receiving a channel recording corresponding to a conversation, receiving a transcription for the conversation, generating a conversation-specific language model for the conversation using the transcription, and conducting speech recognition on the channel recording using the conversation-specific language model to provide time boundaries and written language corresponding to utterances within the channel recording. The method further includes determining sentence or phrase boundaries for the transcription, aligning written language within the one or more transcriptions with the written language corresponding to the utterances with the channel recording to provide sentence or phrase boundaries for the channel recording, and training a speech recognizer according to the sentence or phrase boundaries for the transcription and the sentence or phrase boundaries for the channel recording. A computer system and computer program product corresponding to the method are also disclosed herein.

Acoustic model training

An automatic speech recognition system and a method performed by an automatic speech recognition system are provided. The method includes performing at least two passes of speech activity detection on an acoustic utterance uttered by a speaker. The at least two passes include an initial pass and a subsequent pass. The method further includes estimating at least one of feature statistics and transforms for acoustic feature extraction and acoustic modeling based on an output of an initial pass. The method further includes performing automatic speech recognition using an output of the subsequent pass while bypassing an output of the initial pass to recognize the acoustic utterance.

Multi-pass speech activity detection strategy to improve automatic speech recognition

Systems and methods for spoken term detection are provided. A method for spoken term detection, comprises receiving phone level out-of-vocabulary (OOV) keyword queries, converting the phone level OOV keyword queries to words, generating a confusion network (CN) based keyword searching (KWS) index, and using the CN based KWS index for both in-vocabulary (IV) keyword queries and the OOV keyword queries.

Method and system for efficient spoken term detection using confusion networks

A method for spoken term detection, comprising generating a time-marked word list, wherein the time-marked word list is an output of an automatic speech recognition system, generating an index from the time-marked word list, wherein generating the index comprises creating a word loop weighted finite state transducer for each utterance, i, receiving a plurality of keyword queries, and searching the index for a plurality of keyword hits.

Method and system for order-free spoken term detection

Systems and methods for processing a query include determining a plurality of sets of match candidates for a query using a processor, each of the plurality of sets of match candidates being independently determined from a plurality of diverse word lattice generation components of different type. The plurality of sets of match candidates is merged by generating a first score for each match candidate to provide a merged set of match candidates. A second score is computed for each match candidate of the merged set based upon features of that match candidate. The first score and the second score are combined to provide a final set of match candidates as matches to the query.

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New York, NY, United States of America

Lidia Mangu