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.

引用

页码：236 / +

页数：3

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