Automatic detection of laryngeal pathologies using cepstral analysis in Mel and Bark scales

Abstract

Problems in voice production can appear due to functional disorders and laryngeal pathologies. The presence of laryngeal pathologies can causes significant changes in the vibrational patterns of the vocal folds and it is demonstrated that the impact of such pathologies can be reduced through continuous speech therapy. We propose a methodology based on non-parametric cepstral coefficients in Mel and Bark scales. The most relevant features are automatically selected using two algorithms, one is based on Principal Components Analysis (PCA) and other is based on Sequential Floating Features Selection (SFFS). In order to decide whether a voice recording is healthy or pathological, four different classifiers are implemented: linear and quadratic Bayesian, K nearest neighbors and Parzen. The best result was 89.18%, it was obtained from the union between MFCC and BFCC.