The interaction of hearing loss and level-dependent hearing protection on speech recognition in noise
Bibliographic record
Abstract
OBJECTIVES: To determine the effects of different control settings of level-dependent hearing protectors on speech recognition performance in interaction with hearing loss. DESIGN: Controlled laboratory experiment with two level-dependent devices (Peltor® PowerCom Plus™ and Nacre QuietPro®) in two military noises. STUDY SAMPLE: Word recognition scores were collected in protected and unprotected conditions for 45 participants grouped into four hearing profile categories ranging from within normal limits to moderate-to-severe hearing loss. RESULTS: When the level-dependent mode was switched off to simulate conventional hearing protection, there were large differences across hearing profile categories regarding the effects of wearing the devices on speech recognition in noise; participants with normal hearing showed little effect while participants in the most hearing-impaired category showed large decrements in scores compared to unprotected listening. Activating the level-dependent mode of the devices produced large speech recognition benefits over the passive mode at both low and high gain pass-through settings. The category of participants with the most impaired hearing benefitted the most from the level-dependent mode. CONCLUSIONS: The findings indicate that level-dependent hearing protection circuitry can provide substantial benefits in speech recognition performance in noise, compared to conventional passive protection, for individuals covering a wide range of hearing losses.
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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) | 0.000 | 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 | 0.000 |
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".