Acoustic Correlates of Fatigue in Laryngeal Muscles: Findings for a Criterion-Based Prevention of Acquired Voice Pathologies
Bibliographic record
Abstract
PURPOSE: The objective was to identify acoustic correlates of laryngeal muscle fatigue in conditions of vocal effort. METHOD: In a previous study, a technique of electromyography (EMG) served to define physiological signs of "voice fatigue" in laryngeal muscles involved in voicing. These signs correspond to spectral changes in contraction potentials. A corpus of vocalizations from the 7 participants in the EMG study was used to explore the effects of muscle fatigue on voice acoustics. Each participant produced vocalizations at regular intervals (50 in all) extending across a day (12-14 hr). The participants also produced 5 min of loud speech with peaks of 74 dBA at 1 m between each vocalization. Twenty acoustic parameters were measured using the Multi-Dimensional Voice Program (Kay Elemetrics, Lincoln Park, NJ). RESULTS: The analyses showed no consistent correlations between acoustic parameters and estimates of muscle fatigue. However, in all cases, nonlinear jumps occurred in the frequency of amplitude tremor at points where fatigue estimates showed a critical shift. These jumps were robust despite changes in F0 in some individuals. CONCLUSION: A brief rise in voice tremor can correspond to a critical change in laryngeal muscle tissues seen as a condition where continued vocal effort can increase the risk of lesions or other conditions affecting voice.
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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 | 0.001 | 0.005 |
| 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 | 0.000 | 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".