D-MAP: a distance-normalized MAP estimation of speaker models for automatic speaker verification

In this paper we introduce a MAP estimation of speaker models in Automatic Speaker Verification with a distance constraint: the D-MAP adaptation. The D-MAP is based on the Kullback-Leibler distances and provides an easy way to automatically compute a speaker-dependent adaptation of the model parameters. We formulate a distance constrained MAP criterion and we show an equivalence between the D-MAP adaptation and the score normalization called D-Norm. From the results obtained with the D-MAP technique, we show that this method gives better performance than a classical speakerindependent MAP adaptation. It is also found that the D-MAP based system without score normalization performs similarly to a classical MAP system with a modelbased score normalization.