A new method for estimation of hidden Markov model parameters

Author

Gao, Yu Qing ; Chen, Yong Bin ; Huang, Tai Yi

Author_Institution

Nat. Lab. of Pattern Recognition, Chinese Acad. of Sci., Beijing, China

Volume

ii

fYear

1990

fDate

16-21 Jun 1990

Firstpage

27

Abstract

An algorithm for estimating the parameters of a hidden Markov model (HMM) is presented. In this algorithm, the rule of parameter estimation is used to maximize the recognition accuracy or to minimize the probability of error of the recognizer which is based on hidden Markov models instead of maximizing the likelihood function in maximum likelihood estimates (MLEs). The performance of minimum probability error (MPE) estimates is better than that of MLEs. Since MPE is much more complex in computation than an MLE, a simplified implementation of MPE which is much more moderate in computation is given. Experiments on speech recognition based on HMMs show that the accuracy of the recognizer trained by the estimation method has about a 5% improvement over the MLE

Keywords

Markov processes; error analysis; parameter estimation; probability; speech recognition; hidden Markov model; maximum likelihood estimates; minimum probability error; parameter estimation; probability; speech recognition; Automation; Biological system modeling; Environmental factors; Error analysis; Hidden Markov models; Maximum likelihood estimation; Parameter estimation; Pattern recognition; Random processes; Speech recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition, 1990. Proceedings., 10th International Conference on

Conference_Location

Atlantic City, NJ

Print_ISBN

0-8186-2062-5

Type

conf

DOI

10.1109/ICPR.1990.119323

Filename

119323