Grid search analysis of nu-SVC for text-dependent speaker-identification

Recent research has strongly established the application of Support Vector Machines for Speaker Recognition. In this paper, we present the variations in efficiency of a model for various parameters of nu-SVC for text-dependent speaker-identification. Radial Basis Function (RBF), sigmoid and polynomial kernels have been used for classification. A statistical comparison between all the three kernels has been shown, highlighting the dependence of each on SVM parameters such as gamma, degree of polynomial and nu. For feature extraction, LPC, MFCC and a combination of both has been employed. The performance of RBF kernel was found to be better than Polynomial as well as Sigmoid Kernel for all feature extraction techniques, with best efficiency for MFCC.