The effect of hearing protection worn by talker and/or target listener on speech production in quiet and noise
Bibliographic record
Abstract
Speech production while wearing hearing protectors poses significant challenges due to their occlusion effect and disruption of the Lombard effect. An experiment was conducted with 24 individuals as they read a list of 12 sentences in open ears and while wearing an earmuff in quiet and in four different noises [pink, International Female Fluctuating Masker (IFFM), speech-spectrum noise (SSnoise), and helicopter] at two levels (70 and 85 dBA). An acoustic manikin, fitted or not with an identical protector, served as the target listener. In noise, speech levels decreased when the talkers wore the earmuff but increased when the target listener was fitted with the earmuff. When the earmuff was used by both the talkers and target listener, speech levels were lower by 3-6 dB at the higher noise level compared to when they were both open ears. Speech levels were typically lower, but extended speech intelligibility index estimates were consistently higher, in fluctuating (IFFM, helicopter) than in continuous noises (pink, SSnoise). Talkers' pitch frequency and voice spectrum measurements followed very closely the changes in speech levels, showing no evidence of compensatory voice modifications. Implications of the lower talker speech levels when wearing hearing protectors are discussed in terms of protector selection, training, and individuals with hearing loss.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".