Although multiple approaches to this problem have been proposed in addition to commercially available speech analytics platforms, there is currently no established measure which incorporates the disparate aspects of affected speech to fully characterize Parkinson's symptom progression, particularly in the prodromal phase.~The measurement concept being evaluated in the present study utilizes a custom smartphone-based speech assessment tool to extract multiple hypothesis-driven acoustic features from patient speech in a real-life environment. The resultant features will be used to train a pair of supervised machine learning models to predict clinical PD symptom severity scores, and to distinguish prodromal PD patients from both healthy matched controls and PD patients in more advanced phases of disease progression.
The aim of this research program is to develop and validate a smartphone app-based digital measurement concept that:~Objectively quantifies the severity of Parkinson's Disease (PD) related vocal and speech symptoms;~Accurately and sensitively identifies vocal and speech abnormalities associated with the prodromal stage of PD.