Automatic Detection of Pathological Voices Using GMM-SVM Method

被引：0

作者：

Wang, Xiang ^{[1
]}

Zhang, Jianping ^{[1
]}

Yan, Yonghong ^{[1
]}

机构：

[1] Chinese Acad Sci, Thinkit Speech Lab, Inst Acoust, Beijing, Peoples R China

来源：

PROCEEDINGS OF THE 2009 2ND INTERNATIONAL CONFERENCE ON BIOMEDICAL ENGINEERING AND INFORMATICS, VOLS 1-4 | 2009年

关键词：

Pathological voices; GMM; SVM; TO-NOISE RATIO;

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：

Modern lifestyle has increased the risk of pathological voices problems. So the therapy of pathological people attracts more attention of people. Meanwhile, acoustic features have been used widely in the therapy of voice disordered people. Classification of Normal and Pathological people is also an auxiliary therapy operation. MFCC has been proved to be a useful feature with traditional classifier such as GMM or HMM. However, the precision rate of the classification can still be improved. In Pattern Recognition field, GMM-SVM has been an effective classification method. In this study, we found that this classification method is also effective in voice disorder classification. EER was improved from 8.2% of GMM to 6.0% of GMM-SVM.

引用

页码：525 / 528

页数：4

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