Abstract
Alterations in speech and voice are among the earliest symptoms of Parkinson’s Disease (PD). Nevertheless, the rich information carried by patients’ speech and voice is only partially used for diagnosis and clinical decision-making that is currently based on holistic ratings of speech intelligibility. An accurate diagnosis could be supported by the application of fully automated analytic methods and machine learning techniques on speech recordings. However, most of the proposed procedures were designed for highly functional but “artificial” vocal paradigms such as sustained phonation and consider all the considerable amount of features that can be extracted using automatic systems. In this work, we perform PD detection trials using features extracted from connected speech rather than isolated speech units. Moreover, we support the adopted machine learning-based methods with linguistic considerations so as to reduce the number of features to some meaningful ones. The main findings highlight that this procedure allows more accurate, economical and, most importantly, interpretable discrimination.