Within-Class Covariance Normalization for SVM-based Speaker Recognition

Abstract

This paper extends the within-class covariance normalization (WCCN) technique described for training generalized linear kernels. We describe a practical procedure for applying WCCN to an SVM-based speaker recognition system where the input feature vectors reside in a high-dimensional space. Our approach involves using principal component analysis (PCA) to split the original feature space into two subspaces: a low-dimensional “PCA space” and a high-dimensional “PCA-complement space.” After performing WCCN in the PCA space, we concatenate the resulting feature vectors with a weighted version of their PCA-complements. When applied to a state-of-the-art MLLR-SVM speaker recognition system, this approach achieves improvements of up to 22pct. in EER and 28pct. in minimum decision cost function (DCF) over our previous baseline. We also achieve substantial improvements over an MLLR-SVM system that performs WCCN in the PCA space but discards the PCA-complement.
Index Terms: kernel machines, support vector machines, feature normalization, generalized linear kernels, speaker recognition.