Enhancing speaker authentication systems using circular hidden Markov models

In this work, the circular hidden Markov models (CHMMs) have been used to enhance the recognition performance of isolated-word text-dependent speaker authentication systems under the neutral talking condition. Our results show that CHMMs enhance the speaker authentication performance under such a condition compared to the left-to-right hidden Markov models (LTRHMMs). The average speaker authentication performance based on using CHMMs has been noticeably improved compared to that based on using LTRHMMs.