Voice disorders detection through multiband cepstral features of sustained vowel

This study aims to detect voice disorders related to vocal fold nodule, Reinke’s edema and neurological pathologies through multiband cepstral features of the sustained vowel /a/. Detection is performed between pairs of study groups and multiband analysis is accomplished using the wavelet transform. For each pair of groups, a parameters selection is carried out. Time series of the selected parameters are used as input for four classifiers with leave-one-out cross validation. Classification accuracies of 100% are achieved for all pairs including the control group, surpassing the state-of-art methods based on cepstral features, while accuracies higher than 88.50% are obtained for the pathological pairs. The results indicated that the method may be adequate to assist in the diagnosis of the voice disorders addressed. The results must be updated in the future with a larger population to ensure generalization.