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 paramcterised feature sets (PFS) to form speaker models. During testing, the likelihood of a speaker, given a set of speaker models is her score. Performance scores of 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 results obtained from the PFS and the final identification is performed on a smaller population set. Results obtained using 2-best list indicate performance improvement.