Speaker Verification using Support Vector Machines

Abstract

Support vector machines (SVM) have become a very popular pattern recognition algorithm for speech processing. In this paper we describe an application of SVMs to speaker verification. Traditionally speaker verification systems have used hidden Markov models (HMM) and Gaussian mixture models (GMM). These classifiers are based on generative models and are prone to overfitting. They do not directly optimize discrimination. SVMs, which are based on the principle of structural risk minimization, consist of binary classifiers that maximize the margin between two classes. The power of SVMs lie in their ability to transform data to a higher dimensional space and to construct a linear binary classifier in this space. Experiments were conducted on the NIST 2003 speaker recognition evaluation dataset. The SVM training was made computationally feasible by selecting only a small subset of vectors for building the out-of-class data. The results obtained using the SVMs showed a 9% absolute improvement in equal error rate and a 33% relative improvement in minimum detection cost function when compared to a comparable HMM baseline system