Incorporating Model-Specific Score Distribution in Speaker Verification Systems

摘要

It has been shown that the authentication performance of a biometric system is dependent on the models/templates specific to a user. As a result, some users may be more easily recognized or impersonated than others. The various categories of users have been characterized by Doddington (1988). We refer to this unbalanced performance across users as the Doddington''s zoo effect. In the context of fusion, we argue that this effect is system-dependent, i.e., a user model that is easily impersonated (a lamb) in one system may be easily recognized in another system (a sheep). While in principle, a fusion system could be trained to cope with the changing animal behavior of users from system to system, the lack of training data makes it impossible. We believe that one major cause of the Doddington''s zoo effect is the variation of class conditional scores from one speaker model to another. We propose a two-level fusion framework that effectively realizes a fusion classifier adapted to each user. First, one applies a client-specific (or model-specific) score normalization procedure to each of the system outputs to be combined. Then, one feeds the resulting normalized outputs to a fusion classifier (common to all users) as input to obtain a final combined score. Two existing model-specific score normalization procedures are considered in this framework, i.e., F- and Z-norms. In addition to them, a novel score normalization method called model-specific log-likelihood ratio (MS-LLR) is also proposed. While Z-norm is impostor-centric, i.e., it makes use of only the impostor score statistics, F-norm and the proposed MS-LLR are client-impostor centric, i.e., they consider both the client and impostor score statistics simultaneously. Our findings based on the XM2VTS and the NIST2005 databases show that when client-impostor centric normalization procedures are used to implement the proposed two-level fusion framework, the r-esulting fusion classifier outperforms the conventional fusion classifier (without applying any user-specific score normalization) in the majority of experiments.