Detection of voice disorders based on wavelet and prosody-related properties

This paper presents an approach to detect voice disorders based on wavelet and prosody-related voice properties. First, several statistical measures of the normalized energy contents of the Discrete Wavelet Transform (DWT) coefficients over all voice frames are determined. Then, similar statistical measures of some prosody-related voice properties, such as mean pitch, jitter and shimmer are also computed over all the frames. In order to form a feature vector to be used in both training and testing phases, a set of statistical measure of the normalized energy contents of the DWT coefficients is combined with a set of statistical measure of the extracted prosody-related voice properties. Here, the voice samples under consideration are assumed to be of two categories, namely healthy and disordered thus formulating the problem in the proposed method as a two-class problem to be solved. Finally, the feature vector as obtained above is fed to an Euclidean Distance based classifier to detect the disordered voice. By performing extensive simulations, it is shown that the statistical analysis based on wavelet and prosody-related properties are able to provide effective detection of a variety of voice disorders from the mixture of healthy and disordered voices.