Evaluating the Usefulness of Non-Diagnostic Speech Data for Developing Parkinson's Disease Classifiers
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arXiv
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| Format: | Preprint |
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2025
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| author | Zhong, Terry Yi Janse, Esther Tejedor-Garcia, Cristian Bosch, Louis ten Larson, Martha |
| author_facet | Zhong, Terry Yi Janse, Esther Tejedor-Garcia, Cristian Bosch, Louis ten Larson, Martha |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_18722 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Evaluating the Usefulness of Non-Diagnostic Speech Data for Developing Parkinson's Disease Classifiers Zhong, Terry Yi Janse, Esther Tejedor-Garcia, Cristian Bosch, Louis ten Larson, Martha Audio and Speech Processing Artificial Intelligence Computation and Language Sound 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. |
| title | Evaluating the Usefulness of Non-Diagnostic Speech Data for Developing Parkinson's Disease Classifiers |
| topic | Audio and Speech Processing Artificial Intelligence Computation and Language Sound |
| url | https://arxiv.org/abs/2505.18722 |