Voice Pathology Detection Using the Adaptive Orthogonal Transform Method, SVM and MLP.

Authors

  • Fadwa Abakarim National School of Applied Sciences, Ibn Zohr University, Agadir, Morocco
  • Abdenbi Abenaou National School of Applied Sciences, Ibn Zohr University, Agadir, Morocco

DOI:

https://doi.org/10.3991/ijoe.v17i14.26701

Keywords:

signal processing, voice pathology detection, adaptive orthogonal transform, support vector machine, multilayer perceptron, neural network models

Abstract


In this paper, an automatic voice pathology recognition system is realized. The special features are extracted by the Adaptive Orthogonal Transform method, and to provide their statistical properties we calculated the average, variance, skewness and kurtosis values. The classification process uses two models that are widely used as a classification method in the field of signal processing: Support Vector Machine (SVM) and Multilayer Perceptron (MLP). The proposed system is tested by using a German voice database: the Saarbruecken Voice Database (SVD). The experimental results show that the Adaptive Orthogonal Transform method works perfectly with the Multilayer Perceptron Neural Network, which achieved 98.87% accuracy. On the other hand, the combination of the Adaptive Orthogonal Transform method and Support Vector Machine reached 85.79% accuracy.
Cover iJOE Vol. 17 No. 14 (2021)

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Published

2021-12-14

How to Cite

Abakarim, F., & Abenaou, A. (2021). Voice Pathology Detection Using the Adaptive Orthogonal Transform Method, SVM and MLP. International Journal of Online and Biomedical Engineering (iJOE), 17(14), pp. 90–102. https://doi.org/10.3991/ijoe.v17i14.26701

Issue

Vol. 17 No. 14 (2021)

Section

Papers

License

Copyright (c) 2021 Fadwa Abakarim, Abdenbi Abenaou

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