Robust model for speaker verification against session-dependent utterance variation

This paper investigates a new method for creating speaker models that are robust against utterance variation in continuous distribution hidden Markov model-based speaker verification. In this method, the distribution of the session-independent features for each speaker is estimated by separately modeling the session-to-session utterance variation as two distinct variations: one session-dependent and the other session-independent. In practice, joint normalization of the session-dependent utterance variation and estimation of the parameters of speaker models is performed based on a speaker adaptive training algorithm. The resulting speaker models more accurately represent session-independent speaker characteristics, and the discriminatory capabilities of these models increases. In text-independent speaker verification experiments using data uttered by 20 speakers in 7 sessions over 16 months, we show that the proposed method achieves a 15% reduction in the error rate