DocumentCode :
290372
Title :
Non-linear regression based feature extraction for connected-word recognition in noise
Author :
Seide, F. ; Mertins, A.
Author_Institution :
Telecommun. Group, Tech. Univ. Hamburg-Harburg, Germany
Volume :
ii
fYear :
1994
fDate :
19-22 Apr 1994
Abstract :
This paper shows the application of non-linear regression to robust feature extraction for noisy speech recognition. In this approach, a non-linear estimator is used to compute noise invariant features from non-linear combinations of noise contaminated observations. The observations may be short-term subband-energies obtained from a filter bank analysis, cepstral coefficients of linear prediction coefficients. Instead of training the hidden Markov models (HMMs) under various noise conditions, they can be trained with clean data. The results show that this method leads to error rates comparable to those achieved by training in the presence of noise
Keywords :
Gaussian noise; estimation theory; feature extraction; hidden Markov models; speech recognition; statistical analysis; Gaussian noise; HMM; cepstral coefficients; connected-word recognition; error rates; feature extraction; filter bank analysis; hidden Markov models; linear prediction coefficients; noise contaminated observations; noise invariant features; noisy speech recognition; nonlinear estimator; nonlinear regression; short-term subband-energies; training; Cepstral analysis; Feature extraction; Gaussian noise; Hidden Markov models; Noise level; Noise robustness; Speech enhancement; Speech recognition; State estimation; Working environment noise;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1994. ICASSP-94., 1994 IEEE International Conference on
Conference_Location :
Adelaide, SA
ISSN :
1520-6149
Print_ISBN :
0-7803-1775-0
Type :
conf
DOI :
10.1109/ICASSP.1994.389712
Filename :
389712
Link To Document :
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