A Bayesian Framework for Score Normalization Techniques Applied to Text Independent Speaker Verification

The purpose of this paper is to unify several of the state-of-the-art score normalization techniques applied to text-independent speaker verification systems. We propose a new Bayesian framework for this purpose. The two well-known Zand T-normalization techniques can be easily interpreted in this framework as different ways to estimate score distributions. This is useful as it helps to understand the various assumptions behind these well-known score normalization techniques, and opens the door for yet more complex solutions. Finally, some experiments on the Switchboard database are performed in order to illustrate the validity of the new proposed framework.