A systematic
analysis of user-dependent performance variability in the context of automatic
speaker verification was first studied by Doddington et al(1998). Different
categories of users were identified and labeled as sheep, goats, lambs and
wolves. While this categorization is significant, it does not provide a
criterion to rank the users in a database based on their variability in
performance. In this work we design and evaluate a user-dependent performance
criterion that requires only a limited number of client (i.e., genuine)training
scores. We then extend such a study to formulate a user-specific score
normalization scheme (a variant of the classical F-norm) and show that
user-dependent variabilities can be reduced by employing such a scheme. The results
of 13 experiments confirm the efficacy of the proposed scheme.