Based on Radial Basis Kernel function of Support Vector Machines for speaker recognition

摘要

Support Vector Machine (SVM) is a new statistical learning method, as a speaker recognition method it has unique advantages. In speaker recognition, the selection of kernel function is a key factor to decide SVM's performance. Among them, Radial Basis Function (RBF) kernel function is the widely used kernel function, and it has two parameters: the punishment factor C and kernel parameters kernelpar. In this paper the parameters (C, kernelpar) of the RBF kernel function are adjusted to find the optimal parameters. It can be seen from the research that the time-consuming and recognition rate can both affect the selection of the optimal parameters.