Abstract
For generations, the evaluation of speech abnormalities in neurodegenerative disorders such as Parkinson’s disease (PD) has been limited to perceptual tests or user-controlled laboratory analysis based upon rather small samples of human vocalizations. Our study introduces a fully automated method that yields significant features related to respiratory deficits, dysphonia, imprecise articulation and dysrhythmia from acoustic microphone data of natural connected speech for predicting early and distinctive patterns of neurodegeneration. We compared speech recordings of 50 subjects with rapid eye movement sleep behaviour disorder (RBD), 30 newly diagnosed, untreated PD patients and 50 healthy controls, and showed that subliminal parkinsonian speech deficits can be reliably captured even in RBD patients, which are at high risk of developing PD or other synucleinopathies. Thus, automated vocal analysis should soon be able to contribute to screening and diagnostic procedures for prodromal parkinsonian neurodegeneration in natural environments.
Highlights
Mean Age Men Women Positive history of Parkinson’s disease in family Mean age of disease onset Age of disease onset
The severity of dysarthria in PD and RBD individuals was perceptually described by speech item 18 of the UPDRS III
The results of our work represent the first step toward the development of a fully automated tool for the large-scale evaluation of prodromal vocal impairment due to neurodegeneration
Summary
Mean Age (years) Men Women Positive history of Parkinson’s disease in family Mean age of disease onset (years) Age of disease onset
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