Efficient Parameterization for Automatic Speaker Recognition Using Support Vector Machines

被引：0

作者：

Chakroun, Rania ^{[3
]}

Frikha, Mondher ^{[2
]}

Zouari, Leila Beltaifa ^{[1
]}

机构：

[1] Natl Sch Engn Sousse, Adv Technol Med & Signals ATMS Res Unit, Sousse, Tunisia

[2] Natl Sch Elect & Telecommun Sfax, Adv Technol Med & Signals ATMS Res Unit, Sfax, Tunisia

[3] Natl Sch Engn Sfax, Adv Technol Med & Signals ATMS Res Unit, Sfax, Tunisia

来源：

INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS (ISDA 2016) | 2017年 / 557卷

关键词：

Support vector machines; Limited data; Speaker recognition; Speaker identification; VERIFICATION; IDENTIFICATION;

D O I：

10.1007/978-3-319-53480-0_65

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Recent advances in the field of speaker recognition have proved to highly outperform algorithms. However this performance degrades when limited data are presented. This paper presents examples on how SVM can improve speaker recognition. The main contribution in this approach is the use of new low-dimensional vectors when training data are limited. We show how different kernels function of Support Vector Machines (SVM) can be used to deal a new approach for speaker recognition system. We achieved remarkable results using TIMIT database.

引用

页码：659 / 666

页数：8

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