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arXiv · 2504.17739

Interpretable Early Detection of Parkinson's Disease through Speech Analysis

Lorenzo Simone·Mauro Giuseppe Camporeale·Vito Marco Rubino·Vincenzo Gervasi·Giovanni Dimauro
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Abstract

Parkinson's disease is a progressive neurodegenerative disorder affecting motor and non-motor functions, with speech impairments among its earliest symptoms. Speech impairments offer a valuable diagnostic opportunity, with machine learning advances providing promising tools for timely detection. In this research, we propose a deep learning approach for early Parkinson's disease detection from speech recordings, which also highlights the vocal segments driving predictions to enhance interpretability. This approach seeks to associate predictive speech patterns with articulatory features, providing a basis for interpreting underlying neuromuscular impairments. We evaluated our approach using the Italian Parkinson's Voice and Speech Database, containing 831 audio recordings from 65 participants, including both healthy individuals and patients. Our approach showed competitive classification performance compared to state-of-the-art methods, while providing enhanced interpretability by identifying key speech features influencing predictions.

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Lorenzo Simone, Mauro Giuseppe Camporeale, Vito Marco Rubino, Vincenzo Gervasi, Giovanni Dimauro. 2025-04-24. Interpretable Early Detection of Parkinson's Disease through Speech Analysis. https://arxiv.org/abs/2504.17739

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