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基于Fisher判别字典学习的说话人识别

王伟 韩纪庆 郑铁然 郑贵滨 陶耀

王伟, 韩纪庆, 郑铁然, 郑贵滨, 陶耀. 基于Fisher判别字典学习的说话人识别[J]. 电子与信息学报, 2016, 38(2): 367-372. doi: 10.11999/JEIT 150566
引用本文: 王伟, 韩纪庆, 郑铁然, 郑贵滨, 陶耀. 基于Fisher判别字典学习的说话人识别[J]. 电子与信息学报, 2016, 38(2): 367-372. doi: 10.11999/JEIT 150566
WANG Wei, HAN Jiqing, ZHENG Tieran, ZHENG Guibin, TAO Yao. Speaker Recognition Based on Fisher Discrimination Dictionary Learning[J]. Journal of Electronics & Information Technology, 2016, 38(2): 367-372. doi: 10.11999/JEIT 150566
Citation: WANG Wei, HAN Jiqing, ZHENG Tieran, ZHENG Guibin, TAO Yao. Speaker Recognition Based on Fisher Discrimination Dictionary Learning[J]. Journal of Electronics & Information Technology, 2016, 38(2): 367-372. doi: 10.11999/JEIT 150566

基于Fisher判别字典学习的说话人识别

doi: 10.11999/JEIT 150566
基金项目: 

国家自然科学基金(61071181, 61471145),国家自然科学基金重大研究计划 (91120303)

Speaker Recognition Based on Fisher Discrimination Dictionary Learning

Funds: 

The National Natural Science Foundation of China (61071181, 61471145), The Major Research Plan of the National Natural Science Foundation of China (91120303)

  • 摘要: 稀疏表示已成功应用于说话人识别领域。在稀疏表示中,构造好的字典起着重要的作用。该文将Fisher准则的结构化字典学习方法引入说话人识别系统。在判别字典的学习过程中,每一个字典对应一个类标签,因此同类别训练样本的重构误差较小。同时,保证训练样本的稀疏编码系数类内误差最小,类间误差最大。在NIST SRE 2003数据库上,实验结果表明该算法得到的等错误率是7.62%,基于余弦距离打分的i-vector的等错误率是6.7%。当两个系统融合后,得到的等错误率是5.07%。
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出版历程
  • 收稿日期:  2015-05-13
  • 修回日期:  2015-09-06
  • 刊出日期:  2016-02-19

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