最小二乘向量机在说话人识别中的应用

Application of LS-SVM in Speaker Recognition

说话人识别是语音识别的一种,是当前的研究热点之一。而基于统计学习理论的支持向量机(SVM)方法是一种新的机器学习算法,已成为机器学习研究的热点。讨论了一种改进的SVM即最小二乘向量机(LS-SVM)的方法进行说话人识别研究。研究表明,基于LS-SVM的说话人识别比传统的SVM说话人识别计算复杂度小、效率更高、对说话人识别有很强的适应性。

Speaker recognition is regarded as a kind of voice recognition. It is one of the current research hotspots. The support vector machines（SVM） based on ethe statistical learning theory is a new machine learning algorithm as the hotspots of machine learning research. 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.