Vocal Acoustic Analysis: ANN Versos SVM in Classification of Dysphonic Voices and Vocal Cords Paralysis

João Paulo Teixeira, Nuno Alves and Paula Odete Fernandes
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João Paulo Teixeira: Research Centre in Digitalization and Intelligent Robotics (CEDRI) and Applied Management Research Unit (UNIAG), Instituto Politécnico de Bragança, Bragança, Portugal
Nuno Alves: Instituto Politécnico de Bragança, Bragança, Portugal
Paula Odete Fernandes: Applied Management Research Unit (UNIAG), Instituto Politécnico de Bragança, Bragança, Portugal

International Journal of E-Health and Medical Communications (IJEHMC), 2020, vol. 11, issue 1, 37-51

Abstract: Vocal acoustic analysis is becoming a useful tool for the classification and recognition of laryngological pathologies. This technique enables a non-invasive and low-cost assessment of voice disorders, allowing a more efficient, fast, and objective diagnosis. In this work, ANN and SVM were experimented on to classify between dysphonic/control and vocal cord paralysis/control. A vector was made up of 4 jitter parameters, 4 shimmer parameters, and a harmonic to noise ratio (HNR), determined from 3 different vowels at 3 different tones, with a total of 81 features. Variable selection and dimension reduction techniques such as hierarchical clustering, multilinear regression analysis and principal component analysis (PCA) was applied. The classification between dysphonic and control was made with an accuracy of 100% for female and male groups with ANN and SVM. For the classification between vocal cords paralysis and control an accuracy of 78,9% was achieved for female group with SVM, and 81,8% for the male group with ANN.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jehmc0:v:11:y:2020:i:1:p:37-51

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