Inter-word coarticulation modeling and MMIE training for improved connected digit recognition

Author

Cardin, Régis ; Normandin, Yves ; Millien, Evelyne

Author_Institution

Centre de Recherche Inf. de Montreal, McGill Coll., Montreal, Que., Canada

Volume

2

fYear

1993

fDate

27-30 April 1993

Firstpage

243

Abstract

The authors describe developments by the speech research group at CRIM (Centre de Recherche Informatique de Montreal), in the field of speaker-independent connected digit recognition, using hidden Markov models (HMMs) trained with maximum mutual information estimation (MMIE). The experiments described were all performed on the complete adult portion of the TIDIGITS corpus. Techniques that made it possible to improve greatly the recognition rate are described. New results include a 0.28% word error rate and a 0.84% string error rate with two models per digit (one for male and one for female speakers) using context-dependent discrete HMMs.<>

Keywords

hidden Markov models; learning (artificial intelligence); speech recognition; TIDIGITS corpus; hidden Markov models; interword coarticulation modelling; maximum mutual information estimation; speaker-independent connected digit recognition; string error rate; training; word error rate;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1993. ICASSP-93., 1993 IEEE International Conference on

Conference_Location

Minneapolis, MN, USA

ISSN

1520-6149

Print_ISBN

0-7803-7402-9

Type

conf

DOI

10.1109/ICASSP.1993.319280

Filename

319280