Minimum divergence estimation of speaker prior in multi-session PLDA scoring

Probabilistic linear discriminant analysis (PLDA) has shown to be effective for modeling speaker and channel variability in the i-vector space for text-independent speaker verification. This paper shows that the PLDA scoring function could be formulated as model comparison between an adapted PLDA model and the universal PLDA. Based on this formulation, we show that a more robust adaptation could be attained by adapting the PLDA model through the use of minimum divergence estimate of speaker prior in the latent subspace. Experimental results on NIST SRE'10 and SRE'12 dataset confirm that the proposed method is effective in handling multi-session task. Notably, it is free from the covariance shrinkage problem typically found in the standard multi-session PLDA scoring.