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

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.