Protecting the hearing-impaired worker: speech understanding with electronic hearing protection devices
Bibliographic record
Abstract
Introduction: In many military and civilian occupations, the use of a hearing protection device (HPD) is required while working in noisy environments. When a worker has a pre-existing hearing loss, wearing traditional passive HPDs further impedes auditory communication. Our aim was to study speech understanding in noise with electronic HPDs under simulated hearing loss conditions to assess their effectiveness for use as hearing enhancement devices. Methods: Speech understanding was tested with 18 participants using the Speech Recognition in Noise Test (SPRINT). The three ear conditions were: unprotected, the wearing of an electronic earmuff (3M PELTOR Tactical 6–S), and the wearing of an electronic earplug (INVISIO V60 and an X5 headset). The two listener conditions were normal-hearing SPRINT, and simulated hearing loss SPRINT (SHL SPRINT). In the latter condition, the SPRINT audio files were digitally filtered to simulate a high-frequency hearing loss. Results: Participants obtained the highest scores with ears unprotected, especially in the SHL SPRINT condition. The external microphones of the electronic earmuff and earplug were found to have limited bandwidth, which could have reduced speech clarity and resulted in the low SPRINT scores. Discussion: The electronic HPDs did not improve speech understanding under simulated hearing loss conditions when compared to unprotected hearing. However, in noisy environments where HPDs are required, they provide a benefit over passive HPDs by reducing the risk of overprotection for the hearing-impaired worker.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".