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A brief overview of hidden Markov models is given. The properties of circular hidden Markov models and their application to speaker recognition are discussed. For each person, a distinct reference CHMM is produced using the Baum forward-and-backward algorithm. Classification is carried out by selecting the model with the highest probability as the speaker identification system output. Preliminary testing on a set of ten speakers indicates a performance of about 94% speaker recognition accuracy.>
Zheng et al. (Mon,) studied this question.