Evaluating the Usefulness of Non-Diagnostic Speech Data for Developing Parkinson's Disease Classifiers

Speech-based Parkinson's disease (PD) detection has gained attention for its automated, cost-effective, and non-intrusive nature. As research studies usually rely on data from diagnostic-oriented speech tasks, this work explores the feasibility of diagnosing PD on the basis of speech data not originally intended for diagnostic purposes, using the Turn-Taking (TT) dataset. Our findings indicate that TT can be as useful as diagnostic-oriented PD datasets like PC-GITA. We also investigate which specific dataset characteristics impact PD classification performance. The results show that concatenating audio recordings and balancing participants' gender and status distributions can be beneficial. Cross-dataset evaluation reveals that models trained on PC-GITA generalize poorly to TT, whereas models trained on TT perform better on PC-GITA. Furthermore, we provide insights into the high variability across folds, which is mainly due to large differences in individual speaker performance.

@article{zhong2025_2505.18722,
  title={ Evaluating the Usefulness of Non-Diagnostic Speech Data for Developing Parkinson's Disease Classifiers },
  author={ Terry Yi Zhong and Esther Janse and Cristian Tejedor-Garcia and Louis ten Bosch and Martha Larson },
  journal={arXiv preprint arXiv:2505.18722},
  year={ 2025 }
}