Classification of pathological and normal voice based on linear discriminant analysis

被引：0

作者：

Lee, Ji-Yeoun ^{[1
]}

Jeong, SangBae ^{[1
]}

Hahn, Minsoo ^{[1
]}

机构：

[1] Informat & Commun Univ, Speech & Audio Informat Lab, 119,Munjiro, Taejon, South Korea

来源：

ADAPTIVE AND NATURAL COMPUTING ALGORITHMS, PT 2 | 2007年 / 4432卷

关键词：

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 suggests a new method to improve the performance of the pathological/normal voice classification. The effectiveness of the mel frequency-based filter bank energies using the fisher discriminant ratio (FDR) is analyzed. Also, mel frequency cepstrum coefficients (MFCCs) and the feature vectors through the linear discriminant analysis (LDA) transformation of the filter bank energies (FBE) are implemented. In addition, we emphasize the relation between the pathological voice detection and the feature vectors through the FBE-LDA transformation. This paper shows that the FBE LDA-based GMM is a sufficiently distinct method for the pathological/normal voice classification. The proposed method shows better performance than the MFCC-based GMM with noticeable improvement.

引用

页码：382 / +

页数：2

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