On the use of some divergence measures in speaker recognition

The first motivation for using Gaussian mixture models for text-independent speaker identification is based on the observation that a linear combination of Gaussian basis functions is capable of representing a large class of sample distributions. While this technique gives generally good results, little is known about which specific part of a speech signal best identifies a speaker. This contribution suggests a procedure, based on the Jensen divergence measure, to automatically extract from the input speech signal the part that best contributes to identify a speaker. Experiments conducted using the Spidre database indicate a significant improvement in the performance of the speaker recognition system