How room acoustics impact speech comprehension by listeners with varying English proficiency levels
Bibliographic record
Abstract
The authors previously reported the preliminary results from an investigation on effects of reverberation and noise on speech comprehension by native and non-native English-speaking listeners at ICA, Montreal. The results showed significant main effects of reverberation time (from 0.4 to 1.2 s) and background noise level (three settings of RC-30, 40, and 50). Non-native listeners performed significantly worse than natives on speech comprehension in general. Furthermore, the negative effect of reverberation was more detrimental for non-native than for native English-speaking listeners. However, the preliminary analyses have not yet accounted for effects of English proficiency on speech comprehension in addition to the room acoustic environments tested. All test participants were screened for three measures of English proficiency (listening span, oral comprehension, and verbal abilities). Non-native listeners as a group scored lower on all three proficiency measures than native English-speaking listeners. In this paper, English proficiency is investigated as confounding factor affecting speech comprehension performance alongside noise and reverberation. The results help to further the understanding of how room acoustics impacts speech comprehension by listeners, native and non-native English-speaking, with varying English proficiency levels. [Work supported by a UNL Durham School Seed Grant and the Paul S. Veneklasen Research Foundation.]
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".