  Parkinson's disease (PD) is a progressive degenerative disorder of the
central nervous system characterized by motor and non-motor symptoms. As the
disease progresses, patients alternate periods in which motor symptoms are
mitigated due to medication intake (ON state) and periods with motor
complications (OFF state). The time that patients spend in the OFF condition is
currently the main parameter employed to assess pharmacological interventions
and to evaluate the efficacy of different active principles. In this work, we
present a system that combines automatic speech processing and deep learning
techniques to classify the medication state of PD patients by leveraging
personal speech-based bio-markers. We devise a speaker-dependent approach and
investigate the relevance of different acoustic-prosodic feature sets. Results
show an accuracy of 90.54% in a test task with mixed speech and an accuracy of
95.27% in a semi-spontaneous speech task. Overall, the experimental assessment
shows the potentials of this approach towards the development of reliable,
remote daily monitoring and scheduling of medication intake of PD patients.
