Probabilistic latent prosody analysis for robust speaker verification

In this investigation, two probabilistic latent semantic analyses (PLSA)-based approaches are proposed for use in speaker verification systems to reduce the number of parameters required by prosodic speaker models to (1) estimate reliably speakers' bi-gram models and to (2) reduce the amount of required training and test data. The basic concept is to (1) adopt PLSA to smooth the underlying n-gram-based prosodic speaker models, and to (2) use PLSA to find a compact latent prosody space to represent efficiently the constellation of speakers. The proposed approaches are evaluated on the standard single-speaker detection task of the 2001 NIST Speaker Recognition Evaluation Corpus, where only one 2minute training enrollment speech and 30s test speech on average are available, Experimental results demonstrated that the proposed approach can reduce the required number of bi-gram parameters from 112 to 88 and 63 per speaker and improve the EERs of MAP-GMM and GMM+T-norm from 12.4% and 9.5% to 10.4% and 8.4%, respectively, and finally to 8,1% after fusing all systems.