Automatic voice pathology detection and classification using vocal tract area irregularity

Muhammad, G. and Altuwaijri, G. and Alsulaiman, M. and Ali, Z. and Mesallam, T.A. and Farahat, M. and Malki, K.H. and Al-Nasheri, A. (2016) Automatic voice pathology detection and classification using vocal tract area irregularity. Biocybernetics and Biomedical Engineering, 36 (2). pp. 309-317.

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In this paper, an automatic voice pathology detection (VPD) system based on voice production theory is developed. More specifically, features are extracted from vocal tract area, which is connected to the glottis. Voice pathology is related to a vocal fold problem, and hence the vocal tract area which is connected to vocal folds or glottis should exhibit irregular patterns over frames in case of a sustained vowel for a pathological voice. This irregular pattern is quantified in the form of different moments across the frames to distinguish between normal and pathological voices. The proposed VPD system is evaluated on the Massachusetts Eye and Ear Infirmary (MEEI) database and Saarbrucken Voice Database (SVD) with sustained vowel samples. Vocal tract irregularity features and support vector machine classifier are used in the proposed system. The proposed system achieves 99.22 ± 0.01 accuracy on the MEEI database and 94.7 ± 0.21 accuracy on the SVD. The results indicate that vocal tract irregularity measures can be used effectively in automatic voice pathology detection. © 2016 Na�ȩcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier Sp. z o.o. All rights reserved.

Item Type:Article
Impact Factor:cited By 28
Uncontrolled Keywords:accuracy; adult; Article; automatic voice pathology detection; automation; classification; data base; female; glottis; human; male; mathematical analysis; mathematical model; priority journal; support vector machine; vocal cord; vocal cord disorder; vowel
ID Code:25859
Deposited By: Ms Sharifah Fahimah Saiyed Yeop
Deposited On:27 Aug 2021 13:07
Last Modified:27 Aug 2021 13:07

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