Optical tracking during vocalization reveals a complex pattern of respiration
Bibliographic record
Abstract
‘…no gender differences were found in breathing pattern or chest wall kinematics in any of the phonetic tasks studied. Our results also show that the activity of the control of expiration during phonation is more complex than during exercise…’ Expiration during exercise is driven from the abdomen, but in this issue Binazzi et al. from Firenze and Montreal show that during phonation the pattern of respiration is visibly different. The team devised a quantitative experiment in which the vocal effort required from healthy subjects was increased in stages, by progressing from quiet breathing to reading, thence to singing and high-effort whispering. The motion of the chest and abdomen was then charted by deploying optoelectronic plethysmography, which tracks body markers using computer controlled optics. An elegantly simple and robust model, based on surface meshing of measured marker positions and their excursions, then directly revealed the temporal characteristics of inspiration and expiration. The authors’ results show tidal volumes and expiratory times increasing with the stage of vocalization and increasingly rapid inspiration. For high-effort whisper, the end expiratory thoracic volumes were found to approach the maximal expiratory flow volume curve. Interestingly, it was not only the abdomen driving the extremes of the expiratory process, but also the triple compartments of the chest wall. This was found to be more costal in the female subjects, because of size not gender. Vocalization quite literally looks every bit as complex as it sounds.
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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 | 0.000 |
| 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) | 0.002 | 0.001 |
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".