A comparative study of mixture-Gaussian VQ, ergodic HMMs and left-to-right HMMs for speaker recognition

This paper compares a mixture-Gaussian vector quantisation (VQ) method, ergodic continuous hidden Markov models (CHMMs) and phone-level left-to-right CHMMs for text-independent speaker recognition. These three methods represent a progression of phonetic specificity prior to the generation of probabilities against which speakers are compared. The mixture-Gaussian VQ uses a single distribution for all phones, the ergodic CHMM uses several distributions which have been shown in a previous text-independent speaker recognition study to represent broad phonetic classes, and the phone-based left-to-right CHMM uses many distributions representing the specific phones in the test utterance. Our experiments with speaker recognition on 40 TIMIT speakers show that the recognition rates of the mixture-Gaussian VQ, ergodic CHMMs and phone-based left-to-right CHMMs are 87.5%, 87.5% and 100% respectively