Detection & Classification of Voice Pathology using Electrical Circuit Parameters
Vikas Mittal1, R. K. Sharma2
1Vikas Mittal, Ph.D. Scholar, School of VLSI Design and Embedded Systems, NIT, Kurukshetra, Haryana, India.
2R. K. Sharma, Professor, Department of Electronics and Communication Engineering, NIT, Kurukshetra, Haryana, India.
Manuscript received on February 04, 2019. | Revised Manuscript received on February 14, 2019. | Manuscript published on August 30, 2019. | PP: 3836-3839 | Volume-8 Issue-6, August 2019. | Retrieval Number: F9400088619/19©BEIESP | DOI: 10.35940/ijeat.F9400.088619
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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 classification of pathological voice is a hot topic that has been expected significant consideration. Voice pathology is related with a vocal folds difficulty, and for this reason, the vocal tract area which is joined to vocal folds demonstrate random patterns in case of a pathological voice. This random pattern is considered to distinguish healthy and pathological voices. It is possible to utilize transmission line theory in discovering automatic voice pathology detection by taking into consideration the vocal tract as acoustic lines. The work concentrates on developing a feature extraction for detecting and classifying vocal fold polyp by investigating different vocal tract parameters. In this paper, the vocal tract length and area are utilized for computing electrical parameters of the vocal tract. Furthermore, these electrical parameters are used for the classification of pathological voice. Finally, using electrical parameters 97.3% accuracy is obtained with SVM classifier when compared with 88.2% with the acoustic parameters, 85.3% accuracy considering physical parameters and other methods used in the past. The outcomes demonstrate that electrical parameters of the vocal tract can be utilized all the more successfully with better precision in voice pathology identification.
Keywords: Voice Pathology Detection (VPD), Vocal Tract Area, Vocal tract length, Support Vector Machine (SVM).