Studies on Model Distance Normalization Approach in Text-independent Speaker Verification

Abstract Model distance normalization (D-Norm) is one of the useful score normalization approaches in automatic speaker verification (ASV) systems. The main advantage of D-Norm lies in that it does not need any additional speech data or external speaker population, as opposed to the other state-of-the-art score normalization approaches. But still, it has some drawbacks, e.g., the Monte-Carlo based Kullback-Leibler distance estimation approach in the conventional D-Norm approach is a time consuming and computation costly task. In this paper, D-Norm was investigated and its principles were explored from a perspective different from the original one. In addition, this paper also proposed a simplified approach to perform D-Norm, which used the upper bound of the KL divergence between two statistical speaker models as the measure of model distance. Experiments on NIST 2006 SRE corpus showed that the simplified approach of D-Norm achieves similar system performance as the conventional one while the computational complexity is greatly reduced.