Automatic Detection of Pathological Voices Using GMM-SVM Method

Xiang Wang, Jianping Zhang, Yonghong Yan
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引用次数: 2

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-Pathological voices, GMM, SVM
基于GMM-SVM的病理语音自动检测
现代生活方式增加了病理性声音问题的风险。因此,对病态患者的治疗越来越受到人们的关注。同时,声学特征在语音障碍患者的治疗中得到了广泛的应用。正常与病理分型也是一种辅助治疗操作。MFCC已被证明是传统分类器(如GMM或HMM)的一个有用特征。但是,分类的准确率还有待提高。在模式识别领域,GMM-SVM是一种有效的分类方法。在本研究中,我们发现这种分类方法在语音障碍分类中也是有效的。EER由GMM的8.2%提高到GMM- svm的6.0%。关键词:病理性声音,GMM, SVM
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