Voice Pathology Detection Using Multiresolution Technique

This paper presents an automatic voice pathology detection using multiresolution technique, more specifically using Gabor wavelets. Gabor wavelets can extract information in various scales and orientations, and thereby can effectively encode distinguishable patterns of normal and pathological voice signals. First, the input voice is transformed to frequency domain using frame based Fourier transformation. 2D Gabor filters with different scale and orientation are applied on the Mel-filtered frequency representation. To reduce the dimension of Gabor features, principal component analysis is applied. These features are fed into a support vector machine for classification. In this investigation, we use two different well known databases, MEEI and SVD. The results show that the proposed method outperforms some of the state-of-the-art techniques used for voice pathology detection.