摘要
说话人识别系统在说话人模板的建立过程中由于说话人的语音帧数量太多,往往要进行筛选,通常这种选择是一种基于枚举的大量反复的提取过程,复杂费时且结果往往并不是最优的。而基于统计学习理论的支持向量机(SVM)方法正好克服了这方面的不足。讨论了一种改进的SVM即最小二乘向量机(LSSVM)的方法进行说话人识别研究。研究表明,基于LSSVM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。
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 the Statistical Learning Theory can solve this problem.An improved SVM named the Least Square Support Vector Machines（LSSVM） is discussed in this paper.The experimental results demonstrate that the LSSVM-based speaker recognition is less computational complexity and more efficient than the SVM- based speaker recognition.Then it has high adaptability for the speaker recognition.
出处
《计算机工程与应用》
CSCD
北大核心
2007年第7期49-51,共3页
Computer Engineering and Applications
关键词
说话人识别
最小二乘向量机
核函数
线性预测
speaker recognition
Least Square Support Vector Machines（LSSVM）
kernel function
linear predictive coding
