Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/69283
Type: Artigo de periódico
Title: Improving performance of spectral subtraction in speech recognition using a model for additive noise
Author: Yoma, NB
McInnes, FR
Jack, MA
Abstract: This correspondence addresses the problem of speech recognition with signals corrupted by additive noise at moderate signal-to-noise ratio (SNR). A model for additive noise is presented and used to compute the uncertainty about the hidden clean signal so as to weight the estimation provided by spectral subtraction. Weighted DTW and Viterbi (HMM) algorithms are tested, and the results show that weighting the information along the signal can substantially increase the performance of spectral subtraction, an easily implemented technique, even with a poor estimation for noise and without using any information about the speaker. It is also shown that the weighting procedure can reduce the error rate when cepstral mean normalization is also used to cancel the convolutional noise.
Subject: additive noise
cepstral mean normalization
convolutional noise
speech recognition
spectral subtraction
weighted matching algorithms
Country: EUA
Editor: Ieee-inst Electrical Electronics Engineers Inc
Rights: fechado
Identifier DOI: 10.1109/89.725325
Date Issue: 1998
Appears in Collections:Unicamp - Artigos e Outros Documentos

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