Modeling prior belief for speaker verification SVM systems

Abstract

Support vector machines (SVMs) can be interpreted as a maximum a posteriori (MAP) estimation of a model’s parameters, for an appropriately chosen likelihood function. In the standard formulation for SVM classification and regression problems, the prior distribution on the weight vector is implicitly assumed to be a multidimensional Gaussian with zero mean and identity covariance matrix. In this paper we propose to relax the assumption that the covariance matrix is the identity matrix, allowing it to be a more general block diagonal matrix. In speaker verification, this covariance matrix can be estimated from held-out speakers. We show results on two speaker verification systems: a Maximum Likelihood Linear Regression (MLLR)-based system and a prosodic system. In both cases, the proposed prior model leads to more than 10pct improvement in equal error rate (EER) with respect to results obtained using the standard prior assumptions.

Index Terms: Support Vector Machines, Kernels, Speaker Recognition, Speaker Verification.