The Analysis of Speech Perception with the Use of Hearing Protection Earplugs using the Canadian Digit Triplet Test
Bibliographic record
Abstract
One of the primary contributors to hearing loss is noise. When the daily noise exposure exceeds 85 dBA over a duration of 8 hours, workers need to wear hearing protection to protect themselves against the effects of noise exposure. However, workers in noisy work environments are often reporting difficulties with speech perception when using hearing protection equipment, especially those with pre-existing hearing loss. This poses a dilemma for workers as they need hear important sounds in their work environment and communicate verbally with their colleagues while effectively protecting themselves from loud noises. A pilot doctoral research project was conducted at the University of Ottawa to study this issue. This project aimed at comparing the attenuation produced by earplugs among participants with normal hearing and those with hearing loss in relation to the instructions given to participants on how to fit their earplugs. This pilot project also strived to analyse speech perception with the use of earplugs in both participant groups using the Canadian Digit Triplet Test (CDTT) as a function of the amount of attenuation achieved. The attenuation provided by earplugs was similar between participants with normal hearing and those with hearing loss. Preliminary results also demonstrated that speech becomes harder to perceive when participants wore their earplugs, especially when given instructions on how to fit them properly. In some participants with hearing loss, earplug attenuation leads to an increase in auditory thresholds to a level where simple speech material like digit strings may no longer be audible at normal vocal levels (55-60 dBA). This can pose a safety risk for workers with hearing loss in some situations, as they may be unable to comprehend essential workplace messages due to the additional hearing difficulties induced by the earplugs.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".