Amarnag Subramanya, Zhengyou Zhang, A.C. Surendran, Patrick Nguyen, Mukund Narasimhan, and Alex Acero
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.
In Proc. of the Int. Conf. on Acoustics, Speech, and Signal Processing
Publisher Institute of Electrical and Electronics Engineers, Inc.
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