Title: RECA-PD: A Robust Explainable Cross-Attention Method for Speech-based Parkinson's Disease Classification Show Chinese title

Title: RECA-PD：一种用于基于语音的帕金森病分类的鲁棒可解释交叉注意力方法

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. Show Chinese abstract

Abstract: 帕金森病（PD）全球影响超过1000万人，言语障碍通常在运动症状出现前几年就已存在，使言语成为早期、非侵入性检测的有价值方式。 尽管最近的深度学习模型实现了高准确性，但它们通常缺乏临床使用所需的可解释性。 为了解决这个问题，我们提出了RECA-PD，这是一种新颖、稳健且可解释的交叉注意力架构，结合了可解释的言语特征与自监督表示。 RECA-PD在基于言语的PD检测中达到最先进水平，同时提供了更加一致且更具临床意义的解释。 此外，我们证明在某些言语任务（例如独白）中的性能下降可以通过分割长录音来缓解。 我们的研究结果表明，性能和可解释性不一定相互排斥。 未来的工作将提高非专家对解释的可用性，并探索严重程度估计以增加实际临床相关性。