Multiresolution Features Based Polynomial Kernel Discriminant Analysis for Speaker Recognition

Abstract

This paper describes polynomial kernel subspace approach to speaker recognition systems. Auditory motivated wavelet packet transform is used to derive the desirable speaker features. The nonlinear mapping between the input space and the feature space is implicitly performed using the kernel trick. This nonlinear mapping increases the discrimination capability of a pattern classifier. The use of Mel-scale based and Bark-scale based wavelet packet trees for feature extraction process adds human auditory perception behavior to enhance the classification performance. Experimental results show that the proposed kernel based technique is computationally efficient and performs well with less training data.