Using second-order hidden Markov model to improve speaker identification recognition performance under neutral condition

Author

Shahin, Ismail

Author_Institution

Electr./Electron. & Comput. Eng. Dept., Univ. of Sharjah, United Arab Emirates

Volume

1

fYear

2003

fDate

14-17 Dec. 2003

Firstpage

124

Abstract

In this paper, second-order hidden Markov model (HMM2) has been used and implemented to improve the recognition performance of text-dependent speaker identification systems under neutral talking condition. Our results show that HMM2 improves the recognition performance under neutral talking condition compared to the first-order hidden Markov model (HMM1). The recognition performance has been improved by 9%.

Keywords

cepstral analysis; computational complexity; hidden Markov models; linear predictive coding; speaker recognition; speech coding; cepstral feature analysis; changing statistical characteristics; computational complexity; double stochastic process; linear predictive coding; neutral talking condition; observation vector; recognition performance; second-order hidden Markov model; state transition matrix; text-dependent speaker identification; Computational complexity; Decoding; Electronic mail; Hidden Markov models; Markov processes; Probability density function; Probability distribution; Speaker recognition; Speech analysis; Stochastic processes;

fLanguage

English

Publisher

ieee

Conference_Titel

Electronics, Circuits and Systems, 2003. ICECS 2003. Proceedings of the 2003 10th IEEE International Conference on

Print_ISBN

0-7803-8163-7

Type

conf

DOI

10.1109/ICECS.2003.1301992

Filename

1301992