Feature extracted from wavelet eigenfunction estimation for text-independent speaker recognition

被引：14

作者：

Lung, SY ^{[1
]}

机构：

[1] Chung Kuo Inst Technol, Dept Management Informat Syst, Taipei, Taiwan

来源：

PATTERN RECOGNITION | 2004年 / 37卷 / 07期

关键词：

speaker recognition; wavelet transform; Karhunen-Loeve transform;

D O I：

10.1016/j.patcog.2003.01.003

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A new speaker feature extracted from wavelet eigenfunction estimation is described. The signal is decomposed through interpolating the scaling function. Wavelets can offer a significant computational advantage by reducing the dimensionality of the eigenvalue problem. Our results have shown that this wavelet feature introduced better performance than the other Karhunen-Loeve transfonn (KLT) with respect to the percentages of recognition. (C) 2003 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

引用

页码：1543 / 1544

页数：2

共 5 条

[1] Hard-limited Karhunen-Loeve transform for text independent speaker recognition [J].

Chen, CCT ;

Chen, CT ;

Tsai, CM .

ELECTRONICS LETTERS, 1997, 33 (24) :2014-2016

[2] Further reduced form of Karhunen-Loeve transform for text independent speaker recognition [J].

Lung, SY ;

Chen, CCT .

ELECTRONICS LETTERS, 1998, 34 (14) :1380-1382

[3] Disk distance measure of speaker recognition [J].

Lung, SY .

ELECTRONICS LETTERS, 1997, 33 (20) :1678-1679

[4] Multi-resolution form of SVD for text-independent speaker recognition [J].

Lung, SY .

PATTERN RECOGNITION, 2002, 35 (07) :1637-1639

[5]

Vetterli M., 1995, Wavelets and Subband Coding

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