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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Veröffentlicht in:	IEEE transactions on speech and audio processing Jg. 6; H. 6; S. 579 - 582
Hauptverfasser:	Yoma, N.B., McInnes, F.R., Jack, M.A.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York, NY IEEE 01.11.1998 Institute of Electrical and Electronics Engineers
Schlagworte:	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
Online-Zugang:	Volltext
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