Single-sided approach to discriminative PLDA training for text-independent speaker verification without using expanded i-vector

Probabilistic linear discriminant analysis (PLDA) has shown to be an effective model for disentangling speaker and channel variability in the i-vector space for text-independent speaker verification. The speaker and channel subspaces in the PLDA model are typically trained by optimizing the maximum likelihood (ML) criterion. PLDA assumes that i-vectors are normally distributed, which has shown to be violated in practice. This paper advocates the use of discriminative training, in which both target and non-target classes are taken into account to re-train the parameters. The efficacy of the proposed method is confirmed via experiments conducted on common condition 1 and 5 of the core task as specified in the Speaker Recognition Evaluations (SREs) 2010 conducted by the National Institute for Standards and Technology (NIST).