关键词: 核函数, 线性预测, 说话人识别, 最小二乘向量机

Abstract: The optimal selection of the speech frames is important and necessary to generate the speaker template of the speaker recognition system since the number of the frames is too large.The existing general selection procedures based on the large number of enumeration and many times iteration,are usually complicated and time-consuming,and the result generated by these methods is not always optimal.The Support Vector Machines(SVM) based on ethe Statistical Learning Theory can solve this problem.An improved SVM,the Least Square Support Vector Machines(LS-SVM) is discussed in this paper.The experimental results demonstrate that the LS-SVM-based speaker recognition is less computational complexity and more effient than the SVM-based speaker recognition.Then it has high adaptability for the speaker recognition.

Key words: kernel function, linear predictive coding, speaker recognition, least square support vector machines