Hidden Markov models merging acoustic and articulatory information to automatic speech recognition

Author

Jacob, Bruno ; Senac, Christine

Author_Institution

IRIT, Univ. Paul Sabatier, Toulouse, France

Volume

4

fYear

1996

fDate

3-6 Oct 1996

Firstpage

2313

Abstract

This paper describes a new scheme for robust speech recognition systems where visual information and acoustic features are merged. Using the “pseudo-diphone” as the robust unit, we compare a global hidden Markov model (HMM) and a master/slave HMM through a centisecond preprocessing and through a segmental one. We confirm by experimentation the importance of articulatory features in clean and noisy environments

Keywords

hidden Markov models; merging; speech recognition; visual perception; acoustic features; acoustic information; articulatory information; automatic speech recognition; centisecond preprocessing; global hidden Markov model; information merging; mater/slave hidden Markov model; noisy environments; pseudo-diphone; robust phonetic unit; robust speech recognition systems; segmental preprocessing; visual information; Acoustic noise; Automatic speech recognition; Cepstral analysis; Context modeling; Decoding; Hidden Markov models; Merging; Robustness; Signal processing; Speech recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Spoken Language, 1996. ICSLP 96. Proceedings., Fourth International Conference on

Conference_Location

Philadelphia, PA

Print_ISBN

0-7803-3555-4

Type

conf

DOI

10.1109/ICSLP.1996.607270

Filename

607270