The bilingual advantage in phonetic/phonological learning: A study of bilingual and monolingual patterns in the reproduction of word-final stops in three novel accents of English
Bibliographic record
Abstract
As part of a larger study investigating the acoustic correlates of accentedness in the reproduction of various accents of English by English monolinguals and French-English bilinguals, we explored speakers' ability to imitate and spontaneously reproduce patterns of realization of word-final coronal stops in three different accents: SE England (Sussex) in which these stops are 100% glottalized, and Russian English and South African English, in which these stops exhibit canonical release about 50% of the time. We have so far fully analyzed the Sussex results and partially analyzed the Russian results. The two groups were characterized by different behaviors: while the bilinguals successfully reproduced the Sussex English accent, the monolinguals did not. By contrast, neither of the groups was successful in reproducing the Russian English accent. After considering the characteristics of each group of speakers and each accent, we conclude tentatively that it is the bilinguals, as a group, who were more successful in the phonetic/phonological learning of a new pattern, perhaps as a result of some type of “bilingual advantage.” Based on work by Calabrese (2011) and Krizman et al. (2012), we propose that this advantage stems from longer availability of acoustic information in echoic memory.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".