Classifier Based Early Detection of Pathological Voice

Voice signal processing is a popular tool to detect pathological voice in children. Voice features are first extracted from voice samples and then classifiers are used to discriminate pathological voices from normal voices. However, there is no consensus among the researchers about the voice features and the classifier algorithms that provide a high accuracy. The main contribution of this paper is to determine a suitable set of voice features and classifiers to detect voice disability with a high accuracy. In contrast to other existing works, several discriminative voice features including peaks, pitch, linear predictive coding (LPC) coefficients, Jitter, Shimmer, formants, Mel frequency cepstral coefficients (MFCCs), relative spectral amplitude (RASTA), and perceptual linear prediction (PLP) have been used. We use several classifier algorithms to discriminate pathological voices from healthy ones. We also compare the performances of these classifiers in this work. The results show that an accuracy of 100% can be achieved provided proper voice feature and classifier algorithm are used.