Speech understanding in noise with integrated in-ear and muff-style hearing protection systems
Bibliographic record
Abstract
Integrated hearing protection systems are designed to enhance free field and radio communications during military operations while protecting against the damaging effects of high-level noise exposure. A study was conducted to compare the effect of increasing the radio volume on the intelligibility of speech over the radios of two candidate systems, in-ear and muff-style, in 85-dBA speech babble noise presented free field. Twenty normal-hearing, English-fluent subjects, half male and half female, were tested in same gender pairs. Alternating as talker and listener, their task was to discriminate consonant-vowel-consonant syllables that contrasted either the initial or final consonant. Percent correct consonant discrimination increased with increases in the radio volume. At the highest volume, subjects achieved 79% with the in-ear device but only 69% with the muff-style device, averaged across the gender of listener/talker pairs and consonant position. Although there was no main effect of gender, female listener/talkers showed a 10% advantage for the final consonant and male listener/talkers showed a 1% advantage for the initial consonant. These results indicate that normal hearing users can achieve reasonably high radio communication scores with integrated in-ear hearing protection in moderately high-level noise that provides both energetic and informational masking. The adequacy of the range of available radio volumes for users with hearing loss has yet to be determined.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".