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

Volume

ii

fYear

1994

fDate

19-22 Apr 1994

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;

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.1994.389717

Filename

389717