Navigating the bilingual cocktail party: Interference from background speakers in listeners with varying L1/L2 proficiency
Bibliographic record
Abstract
Cocktail party environments require listeners to tune in to a target voice while ignoring surrounding speakers (maskers), which could present unique challenges for bilingual listeners. Our study recruited English-French bilinguals to listen to a male target speaking French or English, masked by two female voices speaking French, English, or Tamil, or by speech-shaped noise. Listeners performed better with L1 than L2 targets, and relative L1/L2 proficiency acted like a categorical rather than a continuous variable with respect to SRT averaged over maskers. Further, listeners struggled the most with L1 maskers and struggled the least with Tamil maskers. The results suggest that the balanced bilinguals have a slight disadvantage with L1 targets but compensate with a larger advantage with L2 targets, compared to unbalanced bilinguals. This positive net result supports the idea that being a balanced bilingual is helpful in speech-on-speech perception tasks in environments that offer substantial exposure to L2.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".