Modeling the effects of hearing protection on speech recognition as function of noise level and hearing status
Bibliographic record
Abstract
The ability to communicate verbally when wearing hearing protectors is an important aspect to consider for compliance in the use of these devices as well as for safety and operational efficiency in the workplace. Speech recognition with hearing protectors is influenced by many factors such as the overall amount of noise reduction, the slope of the attenuation-frequency function and the presence of hearing loss [Giguère and Berger, Int. J. Audiol. 55, S30-S40 (2016)]. Interestingly, results from subjective and modelling studies sometimes show a benefit of using passive hearing protectors on speech recognition in noise compared to unprotected listening, especially with normal-hearing individuals in high-noise levels. The purpose of this modeling study is to further explore the conditions promoting such a facilitative effect of hearing protectors on speech recognition, with a particular attention to the noise level in the environment and the hearing status of the user. This information is important to better guide the selection of the most appropriate hearing protection device, given the characteristics of the user and workplace noise.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".