Speech perception and production with one level-dependent hearing protector
Bibliographic record
Abstract
Electronic level-dependent hearing protectors aim to enhance communications and situational awareness in the noisy workplace compared to passive hearing protectors. From a listener’s perspective, the fixed attenuation provided by passive hearing protection interferes with speech recognition in noise in users with hearing loss. From a talker’s perspective, they also disrupt the Lombard reflex and induce an occlusion effect, which leads to a reduced vocal output in noise and compounds the problem. This paper presents laboratory data on speech recognition and production in noise with one level-dependent earmuff device. The device was tested in the level-dependent mode at two different gain settings as well as in a powered OFF condition to simulate passive protection within the same device. Level-dependent hearing protection provided significant speech recognition benefits in noise compared to passive protection, especially for users with hearing loss, and performance often exceeded unprotected listening. Talker speech output levels were also found to be higher in noise when the hearing protector was operated in a level-dependent mode compared to passive protection, but not as high as when unprotected, indicating only a partial recovery of speech production mechanisms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".