Speech discrimination in adverse conditions using acoustic knowledge and selectively trained neural networks

Authors:
Yolande Anglade;Dominique Fohr;Jean-Claude Junqua
Affiliations:
CRIN-CNRS & INRIA Lorraine, Vandoeuvre-les-Nancy Cedex, France and SOLLAC, Florange Cedex, France;CRIN-CNRS & INRIA Lorraine, Vandoeuvre-les-Nancy Cedex, France;Speech Technology Laboratory, Division of Panasonic Technologies, Inc., Santa Barbara, California
Venue:
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: speech processing - Volume II
Year:
1993

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A time-delay neural network architecture for isolated word recognition

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Abstract

This work aims at improving the discrimination of confusable words like letters. We propose a new method which computes spectral parameters only in a discriminative part of the words and uses artificial neural networks to perform the recognition. Tests have been conducted on clean speech and Lombard speech with and without additive noise. They show a general improvement of the recognition accuracy compared with a continuous density hidden Markov models (HMM)recognition system.