Speech recognition in noise under hearing protection: A computational study of the combined effects of hearing loss and hearing protector attenuation
Bibliographic record
Abstract
OBJECTIVE: To investigate the effects of hearing protection on speech recognition in noise. DESIGN: Computational study using a speech recognition model that was previously empirically validated. STUDY SAMPLE: Recognition scores were calculated in unprotected and protected conditions for four sets of hearing protector attenuation functions in two different noises, for three simulated hearing profiles illustrative of those anticipated in the noisy workplace. RESULTS: For a normal-hearing profile, recognition scores were not sensitive to the slope of the attenuation function and the overall amount of noise reduction, but protected conditions provided a small but consistent 7-12% benefit compared to unprotected listening. For profiles simulating hearing loss, recognition scores were much more sensitive to the attenuation function. Substantial drops of 30% or more were found compared to unprotected listening in some conditions of steep attenuation slopes and large noise reductions. Attenuation functions modelled from real hearing protectors with nearly-flat attenuation yielded a benefit compared to unprotected listening for all hearing profiles studied. These findings were true in both noises. CONCLUSIONS: Limiting the slope of the hearing protector attenuation function and/or the overall amount of noise reduction is useful and warranted for workers with hearing loss to prevent adverse effects on speech recognition.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".