AI Summary & Key Takeaways
AI InsightThe experimental results show that the test accuracies of SVMs were found as 94.89% and 91.11% for /a/ vowel and “aydınlık” word, respectively.
📌 3 Key Takeaways
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.