On semi-continuous hidden Markov modeling

Author

Huang, Xuedong ; Lee, Kai-Fu ; Hon, Hsiao-Wuen

Author_Institution

Sch. of Comput. Sci., Carnegie Mellon Univ., Pittsburgh, PA, USA

fYear

1990

fDate

3-6 Apr 1990

Firstpage

689

Abstract

The semicontinuous hidden Markov model is used in a 1000-word speaker-independent continuous speech recognition system and compared with the continuous mixture model and the discrete model. When the acoustic parameter is not well modeled by the continuous probability density, it is observed that the model assumption problems may cause the recognition accuracy of the semicontinuous model to be inferior to the discrete model. A simple method based on the semicontinuous model is investigated, to re-estimate the vector quantization codebook without continuous probability density function assumptions. Preliminary experiments show that such reestimation methods are as effective as the semicontinuous model, especially when the continuous probability density function assumption is inappropriate

Keywords

Markov processes; probability; speech recognition; continuous probability density; semicontinuous hidden Markov model; speaker-independent continuous speech recognition; vector quantization codebook; Computer science; Hidden Markov models; Loudspeakers; Pattern classification; Probability density function; Probability distribution; Robustness; Smoothing methods; Speech recognition; Vector quantization;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1990. ICASSP-90., 1990 International Conference on

Conference_Location

Albuquerque, NM

ISSN

1520-6149

Type

conf

DOI

10.1109/ICASSP.1990.115853

Filename

115853