Enhancing speaker identification performance using circular hidden Markov model

In this paper, circular hidden Markov model (CHMM) is implemented to improve the recognition performance of isolated-word text-dependent speaker identification systems under the neural talking condition. Our results show that the CHMM improves the speaker recognition performance under such a condition compared to the left-to-right hidden Markov model (LTRHMM). The average speaker recognition performance has been improved from 90% using the LTRHMM to 95% using the CHMM. In this research, the linear predictive coding (LPC) cepstral feature analysis is used to form the observation vector for both LTRHMM and CHMM.

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