Lucent Technologies Inc.

At+t Corp.

Alexandros Potamianos

Richard Cameron Rose

Vijitha Weerackody

Shrikanth Sambasivan Narayanan

Ilija Zeljkovic

Wolfgang Reichl

A method and apparatus for performing automatic speech recognition (ASR) in a distributed ASR system for use over a wireless channel takes advantage of probabilistic information concerning the likelihood that a given, portion of the data has been accurately decoded to a particular value. The probability of error in each feature in a transmitted feature set is employed to improve speech recognition performance under adverse channel conditions. Bit error probabilities for each of the bits which are used to encode a given ASR feature are used to compute the confidence level that the system may have in the decoded value of that feature. Features that have been corrupted with high probability are advantageously either not used or are weighted less in the acoustic distance computation performed by the speech recognizer. This novel approach to decoding of ASR features is referred to herein as &ldquo;soft feature decoding,&rdquo; and produces dramatic improvements in ASR performance under certain adverse channel conditions.

Soft feature decoding in a distributed automatic speech recognition system for use over wireless channels

An unsupervised, discriminative, sentence level, HMM adaptation based on speech-silence classification is presented. Silence and speech regions are determined either using a speech end-pointer or the segmentation obtained from the recognizer in a first pass. The discriminative training procedure using a GPD or any other discriminative training algorithm, employed in conjunction with the HMM-based recognizer, is then used to increase the discrimination between silence and speech.

Unsupervised HMM adaptation based on speech-silence discrimination

Frequency warping approaches to speaker normalization have been proposed and evaluated on various speech recognition tasks. In all cases, frequency warping was found to significantly improve recognition performance by reducing the mismatch between test utterances presented to the recognizer and the speaker independent HMM model. This invention relates to a procedure which compensates utterances by simultaneously scaling the frequency axis and reshaping the spectral energy contour. This procedure is shown to reduce the error rate in a telephone based connected digit recognition task by 30%.

Combining frequency warping and spectral shaping in HMM based speech

An improved speech recognition system, in which transformation process parameters are generated in response to selected characteristics derived from speech inputs obtained from both carbon and linear microphones. The transformation process parameters are utilized in conjunction with selected digitized speech models to improve the speech recognition process based on the carbon microphone property of suppressing speech spectral energy for low energy invoiced sounds, and also for low energy regions of the spectrum between formant peaks for voices sounds.

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Scotch Plains, NJ, United States of America

Alexandros Potamianos