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Improving performance of spectral subtraction in speech recognition using a model for additive noise
Registro en:
Ieee Transactions On Speech And Audio Processing. Ieee-inst Electrical Electronics Engineers Inc, v. 6, n. 6, n. 579, n. 582, 1998.
1063-6676
WOS:000076653000009
10.1109/89.725325
Autor
Yoma, NB
McInnes, FR
Jack, MA
Institución
Resumen
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. 6 6 579 582