Title :
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
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;
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
DOI :
10.1109/BMEI.2009.5305546
