A Benchmark for Early-stage Parkinson's Disease Detection from Speech
This benchmark addresses the lack of standardized evaluation in speech-based early-stage Parkinson's disease detection, facilitating fair comparisons and clinical adoption for researchers.
The paper introduces the first benchmark for early-stage Parkinson's disease detection from speech, enabling fair and replicable cross-method evaluation across multiple datasets and tasks. It provides multi-dimensional evaluation breakdowns and actionable insights to advance clinically meaningful detection.
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.