SPEAKER VERIFICATION USING TARGET AND BACKGROUND DEPENDENT LINEAR TRANSFORMS AND MULTI-SYSTEM FUSION

摘要

This paper describes a GMM-based speaker verification system that uses speaker-dependent background models transformed by speaker-specific maximum likelihood linear transforms to achieve a sharper separation between the target and the non-target acoustic region. The effect of tying, or coupling, Gaussian components between the target and the background model is studied and shown to be a relevant factor with respect to the desired operating point. A fusion of scores from multiple systems built on different acoustic features via a neural network with performance gains over linear combination is also presented. The methods are experimentally studied on the 1999 NIST speaker recognition evaluation data.