Voiced/unvoiced transitions in speech as a potential bio-marker to detect parkinson's disease

Several studies have addressed the automatic classification of speakers with Parkinson’s disease (PD) and healthy controls (HC). Most of the studies are based on speech recordings of sustained vowels, isolated words, and single sentences. Only few investigations have considered read texts and/or spontaneous speech. This paper addresses two main questions still open regarding the automatic analysis speech in patients with PD, (a) “Is it possible to classify PD patients and HC through running speech signals in multiple languages?”, and (b) “where is the information to discriminate between speech recordings of PD patients and HC?” In this paper speech recordings of read texts and monologues spoken in three different languages are considered. The energy content of the borders between voiced and unvoiced sounds is modeled. According to the results with read texts it is possible to achieve accuracies ranging from 91% to 98% depending on the language. With respect to the results on monologues, the accuracies are above 98% in all of the three languages. The presence of discriminant information in the voiced/unvoiced and unvoiced/voiced transitions is validated here, evidencing the problems of PD patients to stop/start the vocal folds movement during the production of running speech.