Physiologic Features of Vocal Fatigue: Electromyographic Spectral‐Compression in Laryngeal Muscles
Bibliographic record
Abstract
OBJECTIVES: This study addresses the problem of defining observable attributes of "vocal fatigue" as a physiologic condition. The aim was to determine the applicability of electromyography (EMG) spectral compression in observing fatigue in laryngeal muscles arising from prolonged vocal effort. STUDY DESIGN: Single institution, nonrandomized, prospective analysis of subjects evaluated in an academic, tertiary care center. METHODS: In adapting EMG techniques, we report pretest observations that bear on the choice of voicing tasks serving to induce and estimate muscle fatigue and the selection of muscles that are particularly involved in effortful vocalization. On this basis, an experiment was designed where intramuscular EMG was used to record lateral cricoarytenoid potentials of seven subjects at regular intervals across a 12 to 14 hour period (50 samples per subject). Between each of these samples, the participants were required to produce loud speech for 3 minutes with peaks of 74 dBA at 1 meter. RESULTS: The results show fatigue-related spectral compression for all subjects and nonlinear changes across time indicating critical values beyond which fatigue is persistent. CONCLUSION: Spectral compression appears to present a robust attribute of fatigue-related changes in muscles involved in vocalization. There are several implications with respect to research on the prevention of acquired voice pathologies.
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) | 0.000 | 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) | 0.002 | 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".