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

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

URL: http://arxiv.org/abs/2505.18722v1
Date: Sat, 24 May 2025 14:45:55 GMT
Title: Evaluating the Usefulness of Non-Diagnostic Speech Data for Developing Parkinson's Disease Classifiers
Authors: Terry Yi Zhong, Esther Janse, Cristian Tejedor-Garcia, Louis ten Bosch, Martha Larson,
Abstract summary: Speech-based Parkinson's disease (PD) detection has gained attention for its automated, cost-effective, and non-intrusive nature.<n>This work explores the feasibility of diagnosing PD on the basis of speech data not originally intended for diagnostic purposes, using the Turn-Taking dataset.
Score: 5.7624965034085545
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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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