Speaker verification using sequence discriminant support vector machines
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This paper presents a text-independent speaker verification system using support vector machines (SVMs) with score-space kernels. Score-space kernels, generalize Fisher kernels, and are based on an underlying generative model, such as a Gaussian mixture model (GMM). This approach provides direct discrimination between whole sequences, in contrast to the frame-level approaches at the heart of most current systems. The resultant SVMs have a very high dimensionality, since it is related to the number of parameters in the underlying generative model. To ameliorate problems that can arise in the resultant optimization, we introduce a technique called spherical normalization that preconditions the Hessian matrix. We have performed speaker verification experiments using the PolyVar database. The SVM system presented here reduces the relative error rates by 34% compared to a GMM likelihood ratio system.