Title :
Noisy speech recognition using cepstral-time features and spectral-time filters
Author :
Vaseghi, S.V. ; Milner, B.P. ; Humphries, J.J.
Author_Institution :
Sch. of Inf. Syst., East Anglia Univ., Norwich, UK
Abstract :
This paper explores the advantages of using cepstral-time feature matrices, and spectral-time filters, for noisy speech recognition within a hidden Markov model framework. The use of cepstral-time features with spectral subtraction and state-based time-varying Wiener filters is investigated. Experimental results indicate that cepstral-time features, and spectral-time noise processing, provide an effective framework for robust speech recognition in noisy environments
Keywords :
Wiener filters; acoustic noise; cepstral analysis; hidden Markov models; matrix algebra; speech recognition; cepstral-time features; feature matrices; hidden Markov model framework; noise processing; noisy speech recognition; robust speech recognition; spectral subtraction; spectral-time filters; state-based time-varying Wiener filters; Frequency; Hidden Markov models; Information filtering; Information filters; Matrix converters; Noise robustness; Speech enhancement; Speech recognition; Wiener filter; Working environment noise;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1994. ICASSP-94., 1994 IEEE International Conference on
Conference_Location :
Adelaide, SA
Print_ISBN :
0-7803-1775-0
DOI :
10.1109/ICASSP.1994.389717
