Automatic Detection and Classification of Voice Pathology
Vikas Mittal1, R. K. Sharma2
1Vikas Mittal*, Ph.D. Scholar, Department of ECE, School of VLSI Design and Embedded Systems, NIT, Kurukshetra (Haryana) India.
2R. K. Sharma, Professor, Department of ECE, NIT kurukshetra (Haryana) India.
Manuscript received on December 16, 2019. | Revised Manuscript received on December 22, 2019. | Manuscript published on January 10, 2020. | PP: 1155-1159 | Volume-9 Issue-3, January 2020. | Retrieval Number: B7643129219/2020©BEIESP | DOI: 10.35940/ijitee.B7643.019320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The result of rough vocal use is commonly voice pathology. Poor vocal practice can result in worse exceptional of voice, vocal fatigue, and vocal stress. This research utilizes glottal signal (signal produced by vocal folds) parameters to help out in identify voice disorders linked to vocal folds pathologies. For each recorded speech, the respective glottal signal is acquired. We select the most relevant as far as pathological / normal discrimination is concerned from the enormous set of parameters obtained. In this paper a new glottal signal parameter Maximum Opening Quotient (MOQ) is calculated to find Pathological / Normal speech discrimination. Using distinct options, the outcomes are compared. Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) algorithms are used for classifications. Result shows that the average efficiency rise 2.1% using the newly studied glottal parameter Maximum Opening Quotient (MOQ), which is a major contribution of this research.
Keywords: Pathologic Voice, Glottal Signal Parameters, Support Vector Machine (SVM) and K-Nearest Neighbors (KNN)
Scope of the Article: Classification