Local fisher discriminant analysis for spoken language identification

Abstract

I-vector is a state-of-the-art technique widely used in spoken language identification systems. Since i-vectors include total variability factors, discriminant analysis methods have been introduced to find the most discriminative features while removing the undesired variables for language identification, for example, linear discriminant analysis (LDA) and nonparametric discriminant analysis (NDA). However, these methods either do not consider or use weak local structures of the data. In this study, we introduce a local Fisher discriminant analysis (LFDA) as a post-processing discriminant analysis method to extract the discriminative features from i-vectors. LFDA is a full-rank method which takes the local structure of the data into account for non-Gaussian distribution data, i.e., multimodal. Compared with LDA and NDA, LFDA is a pair-wise local method which enhances the centralization of the distribution of samples in the same class to obtain larger amounts of discriminative features. Experimental results indicate that LFDA is more effective than LDA and NDA for the i-vector-based language identification task.