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arXiv · 2505.18722

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

Terry Yi Zhong·Esther Janse·Cristian Tejedor-Garcia·Louis ten Bosch·Martha Larson
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Abstract

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.

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Terry Yi Zhong, Esther Janse, Cristian Tejedor-Garcia, Louis ten Bosch, Martha Larson. 2025-05-24. Evaluating the Usefulness of Non-Diagnostic Speech Data for Developing Parkinson's Disease Classifiers. https://doi.org/10.21437/interspeech.2025-1805

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