Automatic acoustic speech analysis is a promising tool to reliably capture speech changes as it can be used to distinguish neurotypical from pathological speech. Furthermore, it provides non-invasive monitoring of treatment response at low-burden and low-cost. Our aim is to monitor levodopa-related speech changes in Parkinson’s disease and to establish a digital speech-based biomarker that detects these changes.

The project aims to expand the current knowledge of speech motor deficits in people with Parkinson's disease in order to develop speech-related biomarkers for diagnostic and therapeutic speech interventions. The aim is to determine the earliest time of speech motor changes in isolated REM sleep behavior disorder and Parkinson's disease in order to enable early detection of both diseases by means of automated speech recognition and to be able to initiate early therapy.

This project is funded with 100.000 € by the German Parkinson Foundation.

Learn more about the project in this informational video.