| 基于核K-均值聚类和支持向量机结合的说话人识别方法 |
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| 引用本文： | 高争艳,张玉双,王慕坤.基于核K-均值聚类和支持向量机结合的说话人识别方法[J].哈尔滨理工大学学报,2008,13(5). |
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| 作者姓名： | 高争艳 张玉双 王慕坤 |
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| 作者单位： | 哈尔滨理工大学测控技术与通信工程学院,黑龙江,哈尔滨,150040 |
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| 摘 要： | 提出了基于核K-均值聚类方法与支持向量机结合的说话人识别方法,为每两个人建立一个支持向量机,对支持向量机输入的语音信号先进行核K-均值聚类,并选取有效样本作为支持向量机的输入,本文提出的聚类方法能够去更好的聚类并约简数据,提高了识别率.实验比较了在用支持向量机作为分类器的情况下,该核聚类与传统聚类方法的训练速度和识别性能,验证了本文提出方法的有效性.
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| 关 键 词： | 支持向量机 核K-均值聚类 说话人识别 |
Speaker Recognition Using Kernel K-mean Clustering and SVM |
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| GAO Zheng-yan,ZHANG Yu-shuang,WANG Mu-kun.Speaker Recognition Using Kernel K-mean Clustering and SVM[J].Journal of Harbin University of Science and Technology,2008,13(5). |
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| Authors: | GAO Zheng-yan ZHANG Yu-shuang WANG Mu-kun |
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| Affiliation: | GAO Zheng-yan,ZHANG Yu-shuang,WANG Mu-kun(School of Measure-control Technology , Communication Engineering,Harbin University of Science , Technology,Harbin 150040,China) |
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| Abstract: | We proposed a novel approach that is based on the kernel K-means clustering and support vector machine(SVM),set up a support vector machine for each two speakers,here kernel K-means clustering is exploited to input speech signal of SVM into a given amount of clusters choosing the effective samples as the input of SVM.In this paper,we proposed approach acquiring the effective of clustering and reduction of speech data,and it greatly improved recognition rate.The experimental results show the proposed approac... |
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| Keywords: | SVM kernel K-means clustering speaker recognition |
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