Automatic Detection of Pathological Voices Using GMM-SVM Method

Author

Wang, Xiang ; Zhang, Jianping ; Yan, Yonghong

Author_Institution

Thinkit Speech Lab., Chinese Acad. of Sci., Beijing, China

fYear

2009

fDate

17-19 Oct. 2009

Firstpage

1

Lastpage

4

Abstract

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.

Keywords

medical signal processing; patient diagnosis; speech; speech processing; support vector machines; GMM-SVM method; Gaussian mixture model; MFCC; acoustic features; automatic pathological voice detection; auxiliary therapy operation; mel frequency cepstral coefficients; normal voice classification; pathological voice classification; pathological voice problems; pattern recognition; voice disorder classification; voice disordered people; Jitter; Kernel; Medical treatment; Mel frequency cepstral coefficient; Pathology; Signal to noise ratio; Speaker recognition; Speech; Support vector machine classification; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Biomedical Engineering and Informatics, 2009. BMEI '09. 2nd International Conference on

Conference_Location

Tianjin

Print_ISBN

978-1-4244-4132-7

Electronic_ISBN

978-1-4244-4134-1

Type

conf

DOI

10.1109/BMEI.2009.5305546

Filename

5305546