Interpretable Early Detection of Parkinson's Disease through Speech Analysis
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910918224379904 |
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| author | Simone, Lorenzo Camporeale, Mauro Giuseppe Rubino, Vito Marco Gervasi, Vincenzo Dimauro, Giovanni |
| author_facet | Simone, Lorenzo Camporeale, Mauro Giuseppe Rubino, Vito Marco Gervasi, Vincenzo Dimauro, Giovanni |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_17739 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Interpretable Early Detection of Parkinson's Disease through Speech Analysis Simone, Lorenzo Camporeale, Mauro Giuseppe Rubino, Vito Marco Gervasi, Vincenzo Dimauro, Giovanni Machine Learning 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. |
| title | Interpretable Early Detection of Parkinson's Disease through Speech Analysis |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2504.17739 |