Text-dependent speaker identification using circular hidden Markov models

This is the authors' abstract. We don't add key points for this paper.

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.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>