Channel adaptation of plda for text-independent speaker verification

Abstract

Probabilistic linear discriminant analysis (PLDA) has shown to be effective for modeling channel variability in the i-vector space for text-independent speaker verification. Speaker verification is a binary hypothesis testing. Given a test segment, the verification score could be computed as the log-likelihood ratio between a speaker-adapted PLDA and the universal PLDA model. This work proposes to infer the channel factor specific to each test segment and to include the channel estimate in the PLDA models, which essentially shifts the scoring function to better match that of the test channel. We also explore the influence of covariance adaptation in both speaker and channel adaptations. Experimental results on NIST SRE'08 and SRE'10 dataset confirm that the proposed channel adaptation can be effective when the covariance is kept un-adapted, while the covariance adaptation is necessary in the speaker adaptation.