In this presentation, the focus will be on the speech characteristics of people with Parkinson’s disease, highlighting the fact that speech changes should not be considered as a late symptom anymore but as an early marker of motor impairment. Given the heterogeneous presentation of both gross motor and speech motor symptoms, there is significant variability in the data. The individual progression of speech impairments and the diverse responses to treatment options continue to pose a challenge to comprehensively characterizing the ability to speak in people with Parkinson’s disease.

Advancements in machine learning and deep learning techniques have notably improved the capabilities of automatic speech analysis systems, enabling more precise and nuanced assessments of speech. Consequently, leveraging artificial intelligence could address recent research challenges by integrating speech features with clinical characteristics to establish robust, reliable, and objective speech-based biomarkers. This presentation will explore the potential of AI in addressing these challenges, elucidate potential data analysis methodologies, and discuss future applications in this domain.