Abstrakt
Parkinsonism refers to Parkinson's Disease (PD) and Atypical Parkinsonian Syndromes (APS), such as Progressive Supranuclear Palsy (PSP) and Multiple System Atrophy (MSA). Discrimination between PD and APS and within APS groups in early disease stages is a very challenging task.
Interestingly, speech disorder is frequently an early and prominent clinical feature of both PD and APS. This renders speech/voice analysis a promising tool for the development of an objective marker to assist neurologists in their diagnosis.
This paper is a continuation of a recent work on speech-based differential diagnosis within APS. We address the difficult problem of defining disease-specific speech features which is crucial in the perspective of early differential diagnosis.
We investigate this problem by considering the constraint that only a small amount of training data can be available in this setting. To do so, we perform univariate statistical analysis followed by a supervised learning that forces the designed new features to be 1-dimensional.
We carry out experiments using speech recordings of MSA and PSP patients. We show that linear classification models allow the definition of new scalar variables which can be considered as speech features which are specific to each disease, MSA and PSP