Variations in voice level and fundamental frequency with changing background noise level and talker-to-listener distance while wearing hearing protectors: A pilot study
Bibliographic record
Abstract
OBJECTIVE: Speech production in noise with varying talker-to-listener distance has been well studied for the open ear condition. However, occluding the ear canal can affect the auditory feedback and cause deviations from the models presented for the open-ear condition. Communication is a main concern for people wearing hearing protection devices (HPD). Although practical, radio communication is cumbersome, as it does not distinguish designated receivers. A smarter radio communication protocol must be developed to alleviate this problem. Thus, it is necessary to model speech production in noise while wearing HPDs. Such a model opens the door to radio communication systems that distinguish receivers and offer more efficient communication between persons wearing HPDs. DESIGN: This paper presents the results of a pilot study aimed to investigate the effects of occluding the ear on changes in voice level and fundamental frequency in noise and with varying talker-to-listener distance. STUDY SAMPLE: Twelve participants with a mean age of 28 participated in this study. RESULTS: Compared to existing data, results show a trend similar to the open ear condition with the exception of the occluded quiet condition. CONCLUSIONS: This implies that a model can be developed to better understand speech production for the occluded ear.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".