Speaker time-drifting adaptation using trajectory mixture hidden Markov models

Author

Jian Su ; Li, Haizhou ; Haton, Jean-Paul ; Ng, Kai-Tat

Author_Institution

Dept. of Electron. Eng., City Univ. of Hong Kong, Hong Kong

Volume

2

fYear

1996

fDate

7-10 May 1996

Firstpage

709

Abstract

In this paper, a trajectory mixture hidden Markov model (TMHMM) is proposed to cope with the problems of trajectory variability and speaker time-drifting. A theoretical formulation of TMHMM learning is presented. By introducing two pragmatic adaptation schemes, the practical issues which demonstrate the use of the model in capturing the time-drifting of speaker model for speaker recognition are addressed. To evaluate with the YOHO corpus, a set of phonetic units is defined. The effectiveness of the modeling approach is confirmed by a set of experiments. It is shown that an error rate of 0.07% is obtained for closed-set speaker recognition with a total population of 138 talkers. TMHMM can be considered as a special HMM topology dedicated to the time-drifting adaptation problem

Keywords

hidden Markov models; learning systems; maximum likelihood estimation; speaker recognition; speech recognition; TMHMM learning; YOHO corpus; closed-set speaker recognition; phonetic units; pragmatic adaptation schemes; speaker time-drifting adaptation; speech recognition; trajectory mixture hidden Markov model; Bayesian methods; Context modeling; Error analysis; Hidden Markov models; Speaker recognition; Speech processing; Speech recognition; Statistical analysis; Topology; Training data;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1996. ICASSP-96. Conference Proceedings., 1996 IEEE International Conference on

Conference_Location

Atlanta, GA

ISSN

1520-6149

Print_ISBN

0-7803-3192-3

Type

conf

DOI

10.1109/ICASSP.1996.543219

Filename

543219