International Computer Science Institute

U S West Advanced Technologies, Inc.

Qualcomm Incorporated

Nelson H Morgan

Hynek Hermansky

Harinath Garudadri

Petr Motlicek

Philip D Kohn

Sunil Sivadas

Lukas Burget

Stephane N Dupont

Frantisek Grezl

Charles C Wooters

Pratibha Jain

Sachin Kajarekar

Andre Gustavo Adami

Steven Greenberg

Maria Carmen Benitez Ortuzar

Brian E D Kingsbury

A distributed voice recognition system and method for obtaining acoustic features and speech activity at multiple frequencies by extracting high frequency components thereof on a device, such as a subscriber station and transmitting them to a network server having multiple stream processing capability, including cepstral feature processing, MLP nonlinear transformation processing, and multiband temporal pattern architecture processing. The features received at the network server are processed using all three streams, wherein each of the three streams provide benefits not available in the other two, thereby enhancing feature interpretation. Feature extraction and feature interpretation may operate at multiple frequencies, including but not limited to 8 kHz, 11 kHz, and 16 kHz.

Multistream network feature processing for a distributed speech recognition system

An automatic speech recognition apparatus and method with a front end feature extractor that improves recognition performance under adverse acoustic conditions are disclosed. The inventive feature extractor is characterized by a critical bandwidth spectral resolution, an emphasis on slow changes in the spectral structure of the speech signal, and adaptive automatic gain control. In one the feature extractor includes a feature generator configured to compute short-term parameters of the speech signal, a filter system configured to filter the time sequences of the short-term parameters, and a normalizer configured to normalize the filtered parameters with respect to one or more previous values of the filtered parameters. Accordingly, the feature extractor is operable to carry out the following steps: computing short-term parameters of the speech signal; filtering time sequences of the short-term parameters; and normalizing the filtered parameters with respect to one or more previous values of the filtered parameters. The steps of filtering and normalizing preferably are performed independently of one another.

Feature extraction for automatic speech recognition

A method and system are provided for alleviating the harmful effects of convolutional and additive noise in speech, such as due to the environmental noise and linear spectral modification, based on the filtering of time trajectories of an auditory-like spectrum in a particular spectral domain.

Noise resistant auditory model for parametrization of speech

A method and system are provided for alleviating the harmful effects of convolutional distortions of speech, such as the effect of a telecommunication channel, on the performance of an automatic speech recognizer (ASR). The technique is based on the filtering of time trajectories of an auditory-like spectrum derived from the Perceptual Linear Predictive (PLP) method of speech parameter estimation.

Auditory model for parametrization of speech

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Oakland, CA, United States of America

Nelson H Morgan