Feature analysis of dysphonia speech for monitoring Parkinson's disease
Bibliographic record
Abstract
Parkinson's disease (PD) is a progressive neurodegenerative disorder that has no known cure and no known prevention. Early detection is crucial in order to slow down the progress. In the past 10 years, interest in PD analysis has visibly increased. Speech impairment affects the majority of people with Parkinson's (PWP). New features and machine learning algorithms were proposed to help diagnose PD and to measure a patient's progress. Using sustained vowel /a/ recordings, we identified a more prominent set of Mel-Frequency Cepstral Coefficient (MFCC) and Intrinsic Mode Functions (IMF), and other parameters that can best represent the characteristics of Parkinson's dysphonia to assist with the diagnosis process. For higher quality audio signals, there is a visible difference in the higher MFCC coefficients, the wider spectrum bandwidth in the first four IMFs of PWP, and higher power intensity in the healthy subjects. We also found that even when the signals are downsampled into toll-quality, the distinguishable MFCC and IMF features were largely maintained. This enabled a whole possibility of providing telemedicine for PWP.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".