Copyright © 2008 Boštjan Vesnicer and France Mihelič. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

We propose a way of integrating likelihood ratio (LR) decision criterion with nuisance attribute projection (NAP) for Gaussian mixture model- (GMM-) based speaker verification. The experiments on the core test of the NIST speaker recognition evaluation (SRE) 2005 data show that the performance of the proposed approach is comparable to that of the standard approach of NAP which uses support vector machines (SVMs) as a decision criterion. Furthermore, we demonstrate that the two criteria provide complementary information that can significantly improve the verification performance if a score-level fusion of both approaches is carried out.