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

arXiv: https://arxiv.org/abs/2505.18722 · HTML: https://mlsanity.com/paper/2505.18722/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 26 references that are in the map.
- First posted on arXiv 24 May 2025.

## Builds on (most cited first, 7 of 7)

- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [NeuroVoz: a Castillian Spanish corpus of parkinsonian speech](https://mlsanity.com/paper/2403.02371/) (2024)
- [Exploiting Foundation Models and Speech Enhancement for Parkinson's Disease Detection from Speech in Real-World Operative Conditions](https://mlsanity.com/paper/2406.16128/) (2024)
- [Unveiling Interpretability in Self-Supervised Speech Representations for Parkinson's Diagnosis](https://mlsanity.com/paper/2412.02006/) (2024)
- [Innovative Speech-Based Deep Learning Approaches for Parkinson's Disease Classification: A Systematic Review](https://mlsanity.com/paper/2407.17844/) (2024)
- [Speech as a Biomarker for Disease Detection](https://mlsanity.com/paper/2409.10230/) (2024)
- [Convolutional Neural Networks and a Transfer Learning Strategy to Classify Parkinson's Disease from Speech in Three Different Languages](https://mlsanity.com/paper/2002.04374/) (2020)

## Built on by (most cited first, 2 of 2)

- [RECA-PD: A Robust Explainable Cross-Attention Method for Speech-based Parkinson's Disease Classification](https://mlsanity.com/paper/2507.03594/) (2025)
- [Multi-View Speech Representation Learning for Parkinson's Disease Detection Using Context-guided Cross-modal Attention](https://mlsanity.com/paper/2606.09271/) (2026)

## Abstract (arXiv)

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.