Detection of different voice diseases based on the nonlinear characterization of speech signals

This work describes a novel methodology to characterize voice diseases by using nonlinear dynamics, considering different complexity measures that are mainly based on the analysis of the time delay embedded space. The feature space is represented with a DHMM and a further transformation of the DHMM states to a hyperdimensional space is performed. The discrimination between healthy and pathological speech signals is peformed by using a RBF-SVM which is trained following a K-fold cross-validation strategy. Results of around 99% of accuracy are obtained for three different voice disorders, disphonia due to laryngeal pathologies, hypernasality due to cleft lip and palate, and dysarthria due to Parkinson's disease.