Improving SVM by Modifying Kernel Functions for Speaker Identification Task

Support vector machine (SVM) was the first proposed kernel-based method. It uses a kernel function to transform data from input space into a high-dimensional feature space in which it searches for a separating hyperplane. SVM aims to maximise the generalisation ability that depends on the empirical risk and the complexity of the machine. SVM has been widely adopted in real-world applications including speech recognition. In this paper, an empirical comparison of kernel selection for SVM were used and discussed to achieve performance on text-independent speaker identification using the TIMIT corpus. We were focused on SVM trained using linear, polynomial and radial basis function (RBF) kernels. Results showed that the best performance had been achieved by using polynomial kernel and reported a speaker identification rate equal to 82.47%.