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

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2 Author(s)
Ye, Zhihua ; School of Information and Communication Engineering, Beijing Information Science and Technology University, China ; Li, honglian

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.

Published in:

Image and Signal Processing (CISP), 2012 5th International Congress on

Date of Conference:

16-18 Oct. 2012