September 22-25, 1997
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 contribute to identify a speaker. It is shown, by results obtained, that this technique can significantly increase the performance of a speaker recognition system.
Bibliographic reference. Vergin, Rivarol / O'Shaughnessy, Douglas (1997): "A double Gaussian mixture modeling approach to speaker recognition", In EUROSPEECH-1997, 2287-2290.