Communication between native and non-native speakers of English in noise
Bibliographic record
Abstract
Non-fluency has a negative impact on speech understanding in noise, particularly when hearing protection devices are worn. In multi-national military operations where the communication language is English, it is important to understand the effects of non-native speech and accent on speech understanding. Twenty-four normal-hearing participants were divided into two groups: monolingual English speaking from birth (NA group), and those who learned English after the age of 10 (NN group). All participants completed the Language Experience and Proficiency Questionnaire (LEAP-Q; Marian et al., 2007) to confirm their group assignment. Two experimental sessions were completed, in which each participant was paired with an NA participant in one session and an NN participant in the other. The modified rhyme test (MRT) and speech perception in noise test (SPIN) were administered with each participant pair using two methods. In the first, participants spoke to each other using a communication headset (radio) in background noise of 80 dBA. In the second, the particpants wore the headset with the radio off and spoke to each other face-to-face in background noise levels of 55, 60 and 65 dBA. Performance was calculated as the percentage of correct responses. For the MRT, there was a main effect of talker for both the face-to-face (NA- 79.6%; NN-75.2%), and radio conditions (NA-87.2%; NN- 77.5%). There was also a main effect of background noise level for the face-to-face condition (81.1%, 78.3% and 72.8% for the lowest to highest noise levels, respectively). For the SPIN, there was a main effect of the listener in both the face-to-face (NA-70.9%; NN-54.1%) and radio conditions (NA-86.4%; NN-73.8%), as well as a main effect of background noise level for the face-to-face condition (68.4%, 63.5% and 55.7%). Overall, the results indicate that both NA and NN listeners perform poorly when listening to NN talkers.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".