# A Benchmark for Early-stage Parkinson's Disease Detection from Speech

arXiv: https://arxiv.org/abs/2605.14066 · HTML: https://mlsanity.com/paper/2605.14066/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 33 references that are in the map.
- First posted on arXiv 13 May 2026.

## Builds on (most cited first, 10 of 10)

- [NeuroVoz: a Castillian Spanish corpus of parkinsonian speech](https://mlsanity.com/paper/2403.02371/) (2024)
- [Unveiling Interpretability in Self-Supervised Speech Representations for Parkinson's Diagnosis](https://mlsanity.com/paper/2412.02006/) (2024)
- [Innovative Speech-Based Deep Learning Approaches for Parkinson's Disease Classification: A Systematic Review](https://mlsanity.com/paper/2407.17844/) (2024)
- [Area under the ROC Curve has the Most Consistent Evaluation for Binary Classification](https://mlsanity.com/paper/2408.10193/) (2024)
- [Bilingual Dual-Head Deep Model for Parkinson's Disease Detection from Speech](https://mlsanity.com/paper/2503.10301/) (2025)
- [X-vectors: New Quantitative Biomarkers for Early Parkinson's Disease Detection from Speech](https://mlsanity.com/paper/2007.03599/) (2020)
- [Speech as a Biomarker for Disease Detection](https://mlsanity.com/paper/2409.10230/) (2024)
- [PhoneMD: Learning to Diagnose Parkinson's Disease from Smartphone Data](https://mlsanity.com/paper/1810.01485/) (2018)
- [Evaluating the Effectiveness of Pre-Trained Audio Embeddings for Classification of Parkinson's Disease Speech Data](https://mlsanity.com/paper/2506.02078/) (2025)
- [RECA-PD: A Robust Explainable Cross-Attention Method for Speech-based Parkinson's Disease Classification](https://mlsanity.com/paper/2507.03594/) (2025)

## Abstract (arXiv)

Early-stage Parkinson's disease (EarlyPD) detection from speech is clinically meaningful yet underexplored, and published results are hard to compare because studies differ in datasets, languages, tasks, evaluation protocols, and EarlyPD definitions. To address this issue, we propose the first benchmark for speech-based EarlyPD detection, with a speaker-independent split designed for fair and replicable cross-method evaluation on researcher-accessible datasets. The benchmark covers three common speech tasks and evaluates methods under different training-resource settings. We also present multi-dimensional evaluation breakdowns by dataset, aggregation level, gender, and disease stage to support fine-grained comparisons and clinical adoption. Our results provide a replicable reference and actionable insights, encouraging the adoption of this publicly available benchmark to advance robust and clinically meaningful speech-based EarlyPD detection.