A Benchmark for Early-stage Parkinson's Disease Detection from Speech
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 su...
| Authors | Terry Yi Zhong, Cristian Tejedor-Garcia, Khiet P. Truong et al. |
|---|---|
| Primary category | eess.AS |
| Published | 13 May 2026 |
| Last updated at source | 18 July 2026 |
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