In this paper, we propose a new score normalization technique in Automatic Speaker Verification (ASV): the D-Norm. The main advantage of this score normalization is that it does not need any additional speech data nor external speaker population, as opposed to the state-of-the-art approaches. The D-Norm is based on the use of Kullback-Leibler (KL) distances in an ASV context. In a first step. we estimate the KL distances with a Monte-Carlo method and we experimentally show that they are correlated with the verification scores. In a second step, we use this correlation to implement a score normalization procedure, the D-Norm. We analyse its performance and we compare it to that of a conventional normalization, the Z-Norm. The results show that performance of the D-Norm is comparable to that of the Z-Norm. We then conclude about the results we obtain and we discuss the applications of this work.
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