Impact of Respiratory Discomfort on Vocal Quality and Perceived Effort: The Moderating Role of Fear
Bibliographic record
Abstract
Objectives/Hypothesis The goal of this study was to investigate the relationship between respiratory discomfort and voice measures, including perceived vocal effort and selected acoustic parameters. A secondary aim was to examine whether threat appraisal—measured as susceptibility to experience fear of suffocation—modulated these relationships. We hypothesized that greater dyspnea would predict worse voice outcomes, especially in speakers with greater fear susceptibility. Study Design Repeated measures study. Methods Fifty-eight healthy females were submitted to various levels of respiratory discomfort through rounds of breath-holding while they rated their perceived dyspnea. Participants performed a phonation task—in a comfortable and a loud voice—immediately after each breath hold and rated their perceived vocal effort and fear of suffocation. Smoothed cepstral peak prominence (CPPS), harmonics-to-noise ratio (HNR), mean fundamental frequency (mean F 0 ), relative level of high-frequency noise (Hfno), and amplitude difference between the first two harmonics (H 1 -H 2 ) were extracted. Linear mixed models and repeated measures correlations were generated to assess the relationships between dyspnea, fear susceptibility, and voice measures. Results In the vocal effort models for comfortable and loud phonation, dyspnea was a significant predictor ( P < 0.0001) and interacted significantly with fear susceptibility ( P < 0.0001). In the comfortable condition, dyspnea was also found to be a significant predictor for CPPS ( P = 0.0014) and mean F 0 ( P = 0.0003) and interacted significantly with fear susceptibility in the CPPS model ( P = 0.0051). Post hoc analyses showed that perceived vocal effort increased as dyspnea intensified, especially in participants with greater fear susceptibility. The direction of CPPS fluctuations with increasing dyspnea varied based on level of fear susceptibility, although correlations were weak. Conclusions The relationships between respiratory discomfort and voice were influenced by fear, suggesting that sensory and affective mechanisms interact when impacting voice production and vocal effort perception. Future studies could investigate whether similar interactions may impact laryngeal function in voice and upper airway disorders.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".