An alternative normalization scheme in HMM-based text-dependent speaker verification

This paper proposes a normalization scheme for HMM-based text-dependent speaker verification in which the claimed-speaker model score and the background model score are computed for a common alignment made on the speaker-independent model of the password. It is shown that such a normalization preserves some speaker-specific information contained in the alignment and makes the normalization score more consistent in emphasizing remarkable parts of the claimed-speaker model. A special training procedure is proposed. Experiments on a large-scale and realistic telephone database are reported. Finally, first experiments about the integration of an information based on alignment in the decision making are presented. All these results show the interest of the method and encourage further investigation on the speaker modeling in such an approach. (C) 2000 Elsevier Science B.V. Al rights reserved.