Voice pathology detection in continuous speech using nonlinear dynamics

A novel methodology, based on the estimation of nonlinear dynamics features, is presented for automatic detection of pathologies in the phonatory system considering continuous speech records (text-dependent). The proposed automatic segmentation and characterization of the voice registers does not require the estimation of the pitch period, therefore it doesn't depend on the gender and intonation of the patients. A robust methodology for finding the features that better discriminate between healthy and pathological voices and also for analyzing the affinity among them is also presented. An average success rate of 95% ± 3.54% in the automatic detection of voice pathologies is achieved considering only six features. The results indicate that nonlinear dynamics is a good alternative for automatic detection of abnormal phonations in continuous speech.