A New Approach for Detection of Pathological Voice Disorders with Reduced Parameters

Yıl 2018, Cilt: 18 Sayı: 1, 60 - 71, 23.02.2018

Haydar Ankışhan

Öz

Voice data has
demonstrated chaotic behavior in previous studies. Therefore, studying the
linear properties alone does not yield successful results. This is valid for
the examination of voice data as well. Therefore, conducting studies including
chaotic features as well as existing technologies is inevitable. The main
purpose of this study is to detect voice pathologies with fewer special
features using new chaotic features. Both linear and nonlinear characteristics
were used in this study. In this context, the largest Lyapunov exponents and
entropy are preferred as chaotic properties because of their success in
previous studies. Very few results with 100% accuracy were obtained in the
experimental studies. In this study, multiple support vector machines (SVMs)
were selected as a classifier because of their success in previous similar data
types. Thus, the desired accuracy level was achieved using fewer features.
Resultantly, the process complexity decreased and the system speed increased.

Anahtar Kelimeler

Voice disorders, support vector machines, voice disorder detection

Kaynakça

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