Accurate Log-Likelihood Ratio Estimation by using Test Statistical Model for Speaker Verification

In this paper we propose an accurate estimation of the log-likelihood ratio (LLR) thanks to a statistical modelling of the test data. This work takes place within the framework of GMM/UBM based speaker verification. Modelling the test data using a statistical model like a GMM shows several advantages, and particularly it allows to reduce the influence of out-of-domain data thanks to the underlined statistical model. In this paper, we explore the interests of such methods, using a GMM modelling of the test data. We propose also an extension of this approach to the MAP-based speaker model adaptation. Some experiments based on the NIST SRE 2005 protocol are presented and show a significant gain (between 4% and 5% in relative compared to our NIST GMM/UBM baseline) by using our LLR estimation