Receptive vocabulary predicts multilinguals' recognition skills in adverse listening conditions
Bibliographic record
Abstract
Adverse listening conditions are known to affect bilingual listeners' intelligibility scores more than those of monolingual listeners. To advance theoretical understanding of the mechanisms underpinning bilinguals' challenges in adverse listening conditions, vocabulary size and language entropy are compared as predictors in a sentence transcription task with a heterogeneous multilingual population representative of a speech community. Adverse listening was induced through noise type, bandwidth manipulations, and sentences varying in their semantic predictability. Overall, the results generally confirm anticipated patterns with respect to sentence type, noise masking, and bandwidth. Listeners show better comprehension of semantically coherent utterances without masking and with a full spectrum. Crucially, listeners with larger receptive vocabularies and lower language entropy, a measure of the predictability of one's language use, showed improved performance in adverse listening conditions. Vocabulary size had a substantially larger effect size, indicating that vocabulary size has more impact on performance in adverse listening conditions than bilingual language use. These results suggest that the mechanism behind the bilingual disadvantage in adverse listening conditions may be rooted in bilinguals' smaller language-specific receptive vocabularies, offering a harmonious explanation for challenges in adverse listening conditions experienced by monolinguals and multilinguals.
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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 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".