Interpretable Early Detection of Parkinson's Disease through Speech Analysis

• URL: http://arxiv.org/abs/2504.17739v1
• Date: Thu, 24 Apr 2025 16:50:52 GMT
• Title: Interpretable Early Detection of Parkinson's Disease through Speech Analysis
• Authors: Lorenzo Simone, Mauro Giuseppe Camporeale, Vito Marco Rubino, Vincenzo Gervasi, Giovanni Dimauro,
• Abstract summary: We propose a deep learning approach for early Parkinson's disease detection from speech recordings.<n>This approach seeks to associate predictive speech patterns with articulatory features.<n>We evaluated our approach using the Italian Parkinson's Voice and Speech Database, containing 831 audio recordings from 65 participants.
• Score: 0.24466725954625887
• License: http://creativecommons.org/licenses/by/4.0/
• 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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