RECA-PD: A Robust Explainable Cross-Attention Method for Speech-based Parkinson's Disease Classification
Journal:
arXiv
Published Date:
Jul 4, 2025
Abstract
Parkinson's Disease (PD) affects over 10 million people globally, with speech
impairments often preceding motor symptoms by years, making speech a valuable
modality for early, non-invasive detection. While recent deep-learning models
achieve high accuracy, they typically lack the explainability required for
clinical use. To address this, we propose RECA-PD, a novel, robust, and
explainable cross-attention architecture that combines interpretable speech
features with self-supervised representations. RECA-PD matches state-of-the-art
performance in Speech-based PD detection while providing explanations that are
more consistent and more clinically meaningful. Additionally, we demonstrate
that performance degradation in certain speech tasks (e.g., monologue) can be
mitigated by segmenting long recordings. Our findings indicate that performance
and explainability are not necessarily mutually exclusive. Future work will
enhance the usability of explanations for non-experts and explore severity
estimation to increase the real-world clinical relevance.
