Automatic Detection of Parkinson’s Disease with Connected Speech Acoustic Features: towards a Linguistically Interpretable Approach

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.