Abstract
Humans' voice offers the widest variety of motor phenomena of any human activity. However, its clinical evaluation in people with movement disorders such as Parkinson's disease (PD) lags behind current knowledge on advanced analytical automatic speech processing methodology. Here, we use deep learning-based speech processing to differentially analyze voice recordings in 14 people with PD before and after dopaminergic medication using personalized Convolutional Recurrent Neural Networks (p-CRNN) and Phone Attribute Codebooks (PAC). p-CRNN yields an accuracy of 82.35% in the binary classification of ON and OFF motor states at a sensitivity/specificity of 0.86/0.78. The PAC-based approach's accuracy was slightly lower with 73.08% at a sensitivity/specificity of 0.69/0.77, but this method offers easier interpretation and understanding of the computational biomarkers. Both p-CRNN and PAC provide a differentiated view and novel insights into the distinctive components of the speech of persons with PD. Both methods detect voice qualities that are amenable to dopaminergic treatment, including active phonetic and prosodic features. Our findings may pave the way for quantitative measurements of speech in persons with PD.
Highlights
Parkinson’s disease (PD) is a clinically highly variable neurodegenerative disorder
In this work, we have looked at two distinctive machine learning-based speech analysis methodologies to assess the dopaminergic response by classification at the “ON” and “OFF” moments of Persons with PD (PwPD)
Our work aims to explore the feasibility of detecting clinically meaningful dopamine effects in the speech of PwPD with deep learning methods
Summary
Parkinson’s disease (PD) is a clinically highly variable neurodegenerative disorder. Neuronal activity in the basal ganglia and related circuits involved in motor control becomes dysfunctional, resulting in a characteristic motor phenotype with loss of movement amplitude, slowing of movement, and loss of automaticity (Hughes et al, 1992). This loss in motor performance affects the voice in a very distinctive way. Persons with PD (PwPD) speak more softly, slur, may often hesitate in talking, have breathiness and hoarse voice quality, along with imprecise articulation (Logemann et al, 1978).
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