Improving performance of spectral subtraction in speech recognition using a model for additive noise

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. Wei...

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Published in:	IEEE transactions on speech and audio processing Vol. 6; no. 6; pp. 579 - 582
Main Authors:	Yoma, N.B., McInnes, F.R., Jack, M.A.
Format:	Journal Article
Language:	English
Published:	New York, NY IEEE 01.11.1998 Institute of Electrical and Electronics Engineers
Subjects:	Additive noise Applied sciences Cepstral analysis Error analysis Exact sciences and technology Hidden Markov models Information, signal and communications theory Noise cancellation Signal processing Signal to noise ratio Speech processing Speech recognition Telecommunications and information theory Testing Uncertainty Viterbi algorithm Additive noise Viterbi algorithm Fast Fourier transformation Speech recognition Error rate Performance analysis Covariance matrix Markov model Dynamic programming Speech processing
ISSN:	1063-6676
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Abstract	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 dynamic time warping (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.
AbstractList	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 dynamic time warping (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 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 dynamic time warping (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.
Author	McInnes, F.R. Jack, M.A. Yoma, N.B.
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Cites_doi	10.1109/ICASSP.1996.541099 10.1016/0885-2308(89)90027-2 10.1109/ICASSP.1994.389265 10.1109/ICASSP.1997.596151
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Keywords	Additive noise Viterbi algorithm Fast Fourier transformation Speech recognition Error rate Performance analysis Covariance matrix Markov model Dynamic programming Speech processing
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SubjectTerms	Additive noise Applied sciences Cepstral analysis Error analysis Exact sciences and technology Hidden Markov models Information, signal and communications theory Noise cancellation Signal processing Signal to noise ratio Speech processing Speech recognition Telecommunications and information theory Testing Uncertainty Viterbi algorithm
Title	Improving performance of spectral subtraction in speech recognition using a model for additive noise
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