Incorporating Variation of Model-Specific Score Distribution in Speaker Verification Systems

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 et al.(1988). The authors refer to this unbalanced performance across users as the Doddington's zoo effect. In the context of fusion, they 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, 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.