Title :
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
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;
Conference_Titel :
Spoken Language, 1996. ICSLP 96. Proceedings., Fourth International Conference on
Conference_Location :
Philadelphia, PA
Print_ISBN :
0-7803-3555-4
DOI :
10.1109/ICSLP.1996.607270
