On the robust incorporation of formant features into hidden Markov models for automatic speech recognition

Author

Garner, Philip N. ; Holmes, Wendy J.

Author_Institution

Defence Evaluation & Res. Agency, Malvern, UK

Volume

1

fYear

1998

fDate

12-15 May 1998

Firstpage

1

Abstract

A formant analyser is interpreted probabilistically via a noisy channel model. This leads to a robust method of incorporating formant features into hidden Markov models for automatic speech recognition. Recognition equations follow trivially, and Baum-Welch style re-estimation equations are derived. Experimental results are presented which provide empirical proof of convergence, and demonstrate the effectiveness of the technique in achieving recognition performance advantages by including formant features rather than only using cepstrum features

Keywords

feature extraction; frequency estimation; hidden Markov models; probability; speech recognition; Baum-Welch style re-estimation equations; HMM; automatic speech recognition; cepstrum features; convergence; formant analyser; formant features; hidden Markov models; noisy channel model; robust method; Acoustic noise; Automatic speech recognition; Cepstrum; Convergence; Data mining; Equations; Frequency estimation; Frequency measurement; Hidden Markov models; Robustness;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech and Signal Processing, 1998. Proceedings of the 1998 IEEE International Conference on

Conference_Location

Seattle, WA

ISSN

1520-6149

Print_ISBN

0-7803-4428-6

Type

conf

DOI

10.1109/ICASSP.1998.674352

Filename

674352