Effects of hearing loss and language proficiency on speech intelligibility over radio transmission with tactical communication devices
Bibliographic record
Abstract
Objective: Study the effects of hearing loss and language proficiency in a speech task over radio transmission.Design: Four TCAPS device conditions (2 models × 2 talk-through modes) were investigated with the Modified Rhyme Test (MRT) conducted in talker-listener pairs immersed in 85-dBA noise. Speech quality ratings and preferred radio volume levels were also collected.Study sample: Thirty-six participants divided into three groups (control, non-fluent, hearing-impaired) took part in the experiment. Participants acted as talkers and listeners when paired with a unique standard individual (fluent and normal hearing) of the same gender.Results: MRT scores were significantly lower in many device conditions when the non-fluent group of participants acted as listeners and talkers and when the hearing-impaired participants acted as listeners, compared to the control group. MRT results were also consistently poorer with one device configured for bone-conducted voice pick-up in the occluded ear compared to another one equipped with an external mouth microphone. Talk-through settings had little effect. MRT results were reflected in the subjective quality ratings. Participants with hearing loss used higher radio volume levels.Conclusions: Language proficiency, hearing loss and method of sensing the talker’s voice are key issues to consider with TCAPS devices.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.003 | 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".