Voice Pathology Classification Using Machine Learning

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Copy For Citation

Mohammed H. M. A., Ömeroğlu A. N., Polat M., Oral E. A., Özbek İ. Y.

2nd International Symposium on Applied Science and Engineering (ISASE-2021), Erzurum, Turkey, 7 - 09 April 2021, pp.354-357

• Publication Type: Conference Paper / Full Text
• City: Erzurum
• Country: Turkey
• Page Numbers: pp.354-357
• Ataturk University Affiliated: Yes

Abstract

 In this study, the diagnosis of voice diseases affecting the sound quality of many people during their life has been examined. Systems developed for the automatic classification of healthy and pathological sounds are of great interest in early detection of voice disorders. The main purpose of this study is to investigate and compare the performance of these techniques using various machine learning techniques for voice pathology. All analyzes are performed using the Saarbruecken Voice Database. Detection of voice pathology are evaluated in terms of accuracy, sensitivity and specificity. Depending on the characteristics evaluated, the best accuracy is determined as 85.91% by using SVM algorithm.