Speaker Verification can be treated as a statistical hypothesis testing

problem. The most commonly used approach is the likelihood ratio

test (LRT), which can be shown to be optimal using the Neymann-

Pearson lemma. However, in most practical situations the Neymann-

Pearson lemma does not apply. In this paper, we present a more robust

approach that makes use of a hybrid generative-discriminative

framework for text-dependent speaker verification. Our algorithm

makes use of a generative models to learn the characteristics of a

speaker and then discriminative models to discriminate between a

speaker and an impostor. One of the advantages of the proposed algorithm

is that it does not require us to retrain the generative model.

The proposed model, on an average, yields 36.41% relative improvement

in EER over a LRT.

PDF file

In  Proc. of the Int. Conf. on Acoustics, Speech, and Signal Processing

Publisher  Institute of Electrical and Electronics Engineers, Inc.
© 2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

Details