Text-dependent speaker identification using circular hidden Markov models

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.>