# RECA-PD: A Robust Explainable Cross-Attention Method for Speech-based Parkinson's Disease Classification

arXiv: https://arxiv.org/abs/2507.03594 · HTML: https://mlsanity.com/paper/2507.03594/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 28 references that are in the map.
- First posted on arXiv 4 Jul 2025.

## Builds on (most cited first, 14 of 14)

- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Attention-Based Models for Speech Recognition](https://mlsanity.com/paper/1506.07503/) (2015)
- [Neural Additive Models: Interpretable Machine Learning with Neural Nets](https://mlsanity.com/paper/2004.13912/) (2020)
- [Self-Supervised Speech Representation Learning: A Review](https://mlsanity.com/paper/2205.10643/) (2022)
- [Cross Attention Network for Few-shot Classification](https://mlsanity.com/paper/1910.07677/) (2019)
- [CAT: Cross Attention in Vision Transformer](https://mlsanity.com/paper/2106.05786/) (2021)
- [Exploiting Foundation Models and Speech Enhancement for Parkinson's Disease Detection from Speech in Real-World Operative Conditions](https://mlsanity.com/paper/2406.16128/) (2024)
- [Unveiling Interpretability in Self-Supervised Speech Representations for Parkinson's Diagnosis](https://mlsanity.com/paper/2412.02006/) (2024)
- [Higher-order Neural Additive Models: An Interpretable Machine Learning Model with Feature Interactions](https://mlsanity.com/paper/2209.15409/) (2022)
- [Innovative Speech-Based Deep Learning Approaches for Parkinson's Disease Classification: A Systematic Review](https://mlsanity.com/paper/2407.17844/) (2024)
- [Speech as a Biomarker for Disease Detection](https://mlsanity.com/paper/2409.10230/) (2024)
- [Investigating the Effectiveness of Explainability Methods in Parkinson's Detection from Speech](https://mlsanity.com/paper/2411.08013/) (2024)
- [A Survey on Human-Centered Evaluation of Explainable AI Methods in Clinical Decision Support Systems](https://mlsanity.com/paper/2502.09849/) (2025)
- [Evaluating the Usefulness of Non-Diagnostic Speech Data for Developing Parkinson's Disease Classifiers](https://mlsanity.com/paper/2505.18722/) (2025)

## Built on by (most cited first, 1 of 1)

- [A Benchmark for Early-stage Parkinson's Disease Detection from Speech](https://mlsanity.com/paper/2605.14066/) (2026)

## Abstract (arXiv)

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.