Supervector Bayesian speaker comparison

In this paper we propose fully Bayesian speaker comparison of supervectors, which we refer to as SV-BSC, as a method for estimating whether a test cut was generated by the same speaker as an enrollment set. We derive the SV-BSC log-likelihood ratio of same-speaker to different-speaker hypotheses, and present solutions for model training and Bayesian scoring. We then show that if speaker and channel variability are assumed to inhabit a total variability subspace, SV-BSC scoring reduces to a form which requires only low-computation subspace operations. Finally, we show that common speaker recognition techniques such as Joint Factor Analysis (JFA) and i-vector Probabilistic Linear Discriminant Analysis (PLDA) are approximations to this full solution under certain additional assumptions. Experiments on the NIST 2010 SRE show SV-BSC to outperform a PLDA system.