提出可解释的语音帕金森病检测方法，兼顾准确率与临床可用性。
RECA-PD: A Robust Explainable Cross-Attention Method for Speech-based Parkinson's Disease Classification
- 融合可解释语音特征与自监督表示，设计跨注意力架构。
- 在语音帕金森病检测上达到顶尖性能，解释结果更一致且符合临床认知。
- 适用于临床场景，尤其适合需要透明决策的医疗诊断需求。
帕金森病影响全球超1000万人，语音障碍常在运动症状出现前数年显现，使语音成为早期、无创检测的重要模态。尽管近期深度学习模型已实现高准确率，但普遍缺乏临床所需的可解释性。为此，本文提出RECA-PD——一种新颖、鲁棒且可解释的跨注意力架构，结合可解释的语音特征与自监督表征。该方法在语音基帕金森病检测中达到当前最优性能，同时提供更一致、更具临床意义的解释。此外，我们发现通过分割长语音记录可缓解特定任务（如独白）中的性能下降。研究结果表明，性能与可解释性并非互斥。未来工作将提升解释对非专家的可读性，并探索疾病严重程度估计，以增强实际临床价值。
原文摘要 · Abstract (English)
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.
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