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

TL;DR

This paper introduces the first standardized benchmark for early-stage Parkinson's detection from speech, enabling fair comparison and clinical relevance.

Contribution

It provides a comprehensive, speaker-independent benchmark covering multiple speech tasks and evaluation settings for early Parkinson's detection.

Findings

Benchmark facilitates fair comparison across methods.

Multi-dimensional analysis supports clinical insights.

Results serve as a reference for future research.

Abstract

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 EarlyPD detection from speech.