Detection of Early Parkinson's Disease by Leveraging Speech Foundation Models
Bibliographic record
Abstract
Parkinson's disease (PD) is a progressive neurodegenerative disorder affecting millions worldwide, characterized by a wide range of motor and non-motor symptoms. Among these symptoms, alterations in speech and voice quality stand out as early and prominent indicators of the disease. Recently, the emergence of speech foundation models has revolutionized the field by providing powerful tools for speech processing and feature extraction. In this article, we investigate the capabilities of three state-of the art speech foundation models, wav2vec2.0, Whisper and SeamlessM4T, to develop robust and accurate methods for PD detection from voice recordings. We experiment with both direct feature extraction and finetuning of the foundation models for the PD classification task, and validate the results against clinical and neuroimaging data. We achieve promising results using both pretrained features and models' finetuning, with finetuning providing stronger performance, up to 91.35% for AUC, which is the new state of the art on the ICEBERG dataset. The predictions of our models also show good correlation with clinical as well as DaTSCAN scores, proving the feasibility to apply speech foundation models for detection of early PD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".