Analysis of subspace within-class covariance normalization for SVM-based speaker verification

Nuisance attribute projection (NAP) and within-class covariance normalization (WCCN) are two effective techniques for intersession variability compensation in SVM based speaker verification systems. However, by normalizing or removing the nuisance subspace containing the session variability can not guarantee to enlarge the distance between speakers. In this paper, we investigated the probability of using linear discriminant analysis (LDA) for discriminative training. To cope with the small sample size problem which prevents us from using LDA directly, we adapted the subspace LDA approach, which first projects the whole feature space into a relatively low dimensional subspace by PCA, and then performs LDA in the subspace. By some modification, the subspace LDA can be degenerated into a kind of WCCN approach, which we called subspace WCCN. Experiments on NIST SRE tasks showed that, the subspace WCCN outperformed the conventional direct WCCN, especially in low dimensional feature space.