Enhancement of GMM speaker identification performance using complementary feature sets

Abstract

This paper describes a way of enhancing speaker identification (SiD) performance using N-best list method which utilises complementary feature sets. The SiD process is first done by training the Gaussian mixture model (GMM) classifier using parameterised feature sets (PFS) to form speaker models. During testing, the likelihood of a talker, given a set of speaker models is measured. The performance of the SiD system is normally degraded as the population of speakers increases. This paper addresses this problem by using linear prediction cepstral coefficients (LPCC) to complement the errors obtained from the PFS and the final identification is performed on smaller population. Results obtained using 2-best list show performance improvement