[1]但志平 郑胜.最小二乘向量机在说话人识别中的应用[J].计算机技术与发展,2007,(05):30-32.
　DAN Zhi-ping,ZHENG Sheng.Application of LS - SVM in Speaker Recognition[J].,2007,(05):30-32.
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最小二乘向量机在说话人识别中的应用()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2007年05期
页码:
30-32
栏目:
应用开发研究
出版日期:
1900-01-01

文章信息/Info

Title:
Application of LS - SVM in Speaker Recognition
文章编号:
1673-629X（2007）05-0030-03
作者:
但志平1; 2 郑胜1; 2
[1]华中科技大学电信系[2]三峡大学电气信息学院
Author(s):
DAN Zhi-ping;  ZHENG Sheng
[1]Electricity and Info. Dept. ,Huazhong University of Science and Technology[2]Electricity ＆ Info. College,Three Gorges University
关键词:
说话人识别; 最小二乘向量机; 核函数; 线性预测
Keywords:
speaker recognition;  least square support vector machines; kernel function;  linear predictive coding
分类号:
TP391.42
文献标志码:
A
摘要:
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
Abstract:
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

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备注/Memo

备注/Memo:
但志平（1976-），男，湖北赤壁人，硕士，讲师，主要从事语音识别、数字图像处理等方面的研究
更新日期/Last Update: 1900-01-01