Interpretable Early Detection of Parkinson's Disease through Speech Analysis
This work addresses the problem of timely and interpretable diagnosis for patients with Parkinson's disease, though it appears incremental as it builds on existing methods with added interpretability features.
The researchers tackled early detection of Parkinson's disease by analyzing speech impairments using a deep learning approach, achieving competitive classification performance on a dataset of 831 audio recordings from 65 participants while enhancing interpretability by identifying key vocal segments.
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.