An HMM approach to text-prompted speaker verification
ChiWei Che, Qiguang Lin, Dong-Suk Yuk · 2002
This paper presents a speaker recognition system based on hidden Markov models (HMM). The system utilizes concatenated phoneme HMMs and works in a text-prompted mode. Each registered speaker has a separate set of HMMs which are trained using the Baum-Welch algorithm. The speaker recognition system has been evaluated with the YOHO voice verification corpus in terms of both speaker verification and closed-set speaker identification. It is shown that by using 10 seconds of testing speech, an error rate of 0.09% for male and 0.29% for female are obtained for speaker identification with a total population of 138 talkers. For speaker verification, under the 0% false rejection condition, the system achieves a false acceptance rate of 0.09% for male and 0% for female. This paper also studies effects of various factors (such as the mixture number and cohort selection) on the performance of speaker recognition.