Speech recognition in noise under hearing protection: A computational study of the combined effects of hearing loss and hearing protector attenuation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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) | 0.000 | 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 it