Discriminatively trained Bayesian speaker comparison of i-vectors

Abstract

This paper presents a framework for fully Bayesian speaker comparison of i-vectors. By generalizing the train/test paradigm, we derive an analytic expression for the speaker comparison log-likelihood ratio (LLR), as well as solutions for model training and Bayesian scoring. This framework is useful for enrollment sets of any size. For the specific case of single-cut enrollment, it is shown to be mathematically equivalent to probabilistic linear discriminant analysis (PLDA). Additionally, we present discriminative training of model hyper-parameters by minimizing the total cross entropy between LLRs and class labels. When applied to speaker recognition, significant performance gains are observed for various NIST SRE 2010 extended evaluation tasks.