Speaker verification using mixture likelihood profiles extracted from speaker independent hidden Markov models

In this paper, we present a new approach for speaker verification, termed mixture decomposition discrimination (MDD). MDD is based on the idea that, when modeling speech using speaker independent continuous density hidden Markov models (HMM), different speakers speaking the same word would activate different HMM mixture components. One can therefore construct a "mixture profile" of a speaker speaking a given word or phrase. This mixture profile is incorporated into a discriminative training procedure to discriminate between a true speaker and imposters. The effectiveness of MDD is seen when it is incorporated into a hybrid verification system that also includes speaker dependent HMMs with cohort normalization. Experimental results show that the hybrid system reduces the average equal error rate (EER) by 52% when compared with a stand alone cohort-normalized speaker dependent HMM verifier. It is also shown that the computational and model storage requirements needed to incorporate MDD into the hybrid system are relatively small.