Support Vector Machines Approaches and its Application to Speaker Identification

被引：0
|
作者：
Boujelbene, S. Zribi [1 ]
Mezghani, D. Ben Ayed [2 ]
Ellouze, N. [3 ]
机构：
[1] FSHST, Dept Informat, Tunis, Tunisia
[2] ISI, Dept Informat, Tunis, Tunisia
[3] ENIT, Dept Elect Engn, Tunis, Tunisia
来源：
2009 3RD IEEE INTERNATIONAL CONFERENCE ON DIGITAL ECOSYSTEMS AND TECHNOLOGIES | 2009年
关键词：
speaker identification; support vector machines; Gaussian mixture models;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper proposes a classification approach that incorporates the statistical methods GMM and Support Vector Machines. The proposed GMM-SVM system is presented and experimentally evaluated on text independent speaker identification. Our results prove that the combination approach GMM-SVM is significantly superior than SVM approach. We report improvements of 85,37% amelioration in identification rate compared to the SVM identification rate.
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页码：236 / +
页数：3
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