Modelling Speech Intelligibility in the Noisy Work- place for Normal-hearing and Hearing-impaired Listeners Using Hearing Protectors
Bibliographic record
Abstract
A speech intelligibility model was developed and validated for use in workplace environments with hazardous noise levels that require the use of hearing protection devices (HPDs).Two speech perception studies were carried out in laboratory simulations of eight workplace noise environments.The first experiment (n = 32 normal-hearing individuals) was used to develop a general model for speech intelligibility that can be tuned to the specific characteristics of the noise.The second experiment (n = 35) was used to validate the general model for use with listeners covering a wide range of hearing profiles (up to severe hearing loss) and wearing HPDs (earplugs or earmuffs).The model took into account the characteristics of the noise, the signal-to-noise ratio (SNR), the attenuation of the hearing protector, and the hearing status of the listener.Good prediction of speech intelligibility scores in noise with HPDs required the use of correction factors to deal with both audibility (threshold) and distortion (supra threshold) effects arising from hearing loss.Correction factors due to audibility effects were computed from the Speech Intelligibility Index and the pure-tone audiogram.Correction factors due to distortion effects were based on the Hearing-in-Noise Test.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".