Detection of Vocal Cyst Problem by Using High Order Moments and Support Vector Machines

Year 2018, Volume: 6 Issue: 3, 97 - 103, 30.09.2018

Derya Yılmaz Haydar Ankışhan

https://doi.org/10.21541/apjes.380271

Abstract

The voice disorders occurring due to the problems in the
voice producing organs cause some changes in the intensity or tone of the
voice. It is difficult to identify the diseased voice by the reason of its
variable and different nature. One of the most popular voice disorder reasons is cyst
which is located on the vocal cords. The aim of this study is to detect the
vocal cyst problem by using acoustic voices data which were recorded from
healthy people and patient with cyst diagnoses
subjects by using high order statistics and support vector machines
(SVMs) classifier. In this study, two
experimental procedures were implemented for two different voice samples. In
the first, /a/ vowel and the second, the Turkish word of “aydınlık” (mean in
English “bright”) were investigated with skewness and kurtosis parameters which
are third and fourth order cumulants (spectral moments), respectively. The
obtained features values for healthy and cyst subjects were used as the SVMs’ inputs for classification. The experimental results show that the test
accuracies of SVMs were found as 94.89%
and 91.11% for /a/ vowel and “aydınlık” word, respectively. It is concluded from experimental studies that skewness provides more meaningful results than
kurtosis in relation to distinguish into two
voice groups as healthy and cyst.
Additionally, it is assessed that the “aydınlık” is affective word for the pathological and normal acoustic voice
discrimination as good as /a/vowel.

Keywords

Voice disorders, vocal cords cyst, skewness, kurtosis, support vector machines

Detection of Vocal Cyst Problem by Using High Order Moments and Support Vector Machines

Abstract

The voice disorders occurring due to the problems in the voice producing organs cause some changes in the intensity or tone of the voice. It is difficult to identify the diseased voice by the reason of its variable and different nature. One of the most popular voice disorder reasons is cyst which is located on the vocal cords. The aim of this study is to detect the vocal cyst problem by using acoustic voices data which were recorded from healthy people and patient with cyst diagnoses subjects by using high order statistics and support vector machines (SVMs) classifier. In this study, two experimental procedures were implemented for two different voice samples. In the first, /a/ vowel and the second, the Turkish word of “aydınlık” (mean in English “bright”) were investigated with skewness and kurtosis parameters which are third and fourth order cumulants (spectral moments), respectively. The obtained features values for healthy and cyst subjects were used as the SVMs’ inputs for classification. The experimental results show that the test accuracies of SVMs were found as 94.89% and 91.11% for /a/ vowel and “aydınlık” word, respectively. It is concluded from experimental studies that skewness provides more meaningful results than kurtosis in relation to distinguish into two voice groups as healthy and cyst. Additionally, it is assessed that the “aydınlık” is affective word for the pathological and normal acoustic voice discrimination as good as /a/vowel.

Keywords

ses bozuklukları, vokal kist, çarpıklık, basıklık, destek vektör makineler

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