Acoustic correlates of laryngeal control: Parkinson's and healthy older adults
Bibliographic record
Abstract
Vocal fold tension is commonly used to control pulmonary airflow and subglottal pressure, resulting in a positive f0-intensity correlation. Since Parkinson's disease (PD) constrains fine movements, this mechanism may be impaired in PD. We analyzed the f0-intensity correlation in PD and a healthy age- and gender-matched control group. For that, we extracted gender-normalized f0, intensity, and spectral emphasis (SE) from each syllable in three sentences of a read text. Additionally, from a sustained [a] task, we measured maximum phonation time ([a] duration; MPT) as well as jitter and shimmer (combined using PCA; JS). Using Linear Mixed Models, we confirmed the f0-intensity correlation in each group. Furthermore, JS interacted with intensity, indicating that voice instability weakens the correlation. No MPT effect was found. Importantly, even controlling for JS and MPT, the f0-intensity correlation was significantly weaker in PD. Lastly, we build a model using SE instead of intensity and only a negative correlation was found. Overall, this study suggests that voice instability negatively affects airflow control, but is not sufficient to explain its reduction in PD. Moreover, it indicates that the SE-f0 relationship is preserved in PD and it is not affected by the voice parameters analyzed.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".