Speaker Verification Using Adapted User-Dependent Multilevel Fusion

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Abstract

In this paper we study the application of user-dependent score fusion to multilevel speaker recognition. After reviewing related works in multimodal biometric authentication, a new score fusion technique is described. The method is based on a form of Bayesian adaptation to derive the personalized fusion functions from prior user-independent data. Experimental results are reported using the MIT Lincoln Laboratory’s multilevel speaker verification system. It is experimentally shown that the proposed adapted fusion method outperforms both user independent and non-adapted user-dependent fusion approaches.