Text-independent speaker identification based on explicit exploitation of stochastic characteristics of test utterance

Abstract

The benefit of exploiting the stochastic characteristics of test utterance for speaker identification (speaker ID) via cross likelihood ratio and Bayesian information criterion is explored. Simulation results demonstrate the superiority of the proposed approaches over the conventional speaker ID based on maximum likelihood decision rule.