Bilingual Children Shift and Relax Second-Language Phoneme Categorization in Response to Accented L2 and Native L1 Speech Exposure
Bibliographic record
Abstract
Listeners adjust their perception to match that of presented speech through shifting and relaxation of categorical boundaries. This allows for processing of speech variation, but may be detrimental to processing efficiency. Bilingual children are exposed to many types of speech in their linguistic environment, including native and non-native speech. This study examined how first language (L1) Spanish/second language (L2) English bilingual children shifted and relaxed phoneme categorization along the cue of voice onset time (VOT) during English speech processing after three types of language exposure: native English exposure, native Spanish exposure, and Spanish-accented English exposure. After exposure to Spanish-accented English speech, bilingual children shifted categorical boundaries in the direction of native English speech boundaries. After exposure to native Spanish speech, children shifted to a smaller extent in the same direction and relaxed boundaries leading to weaker differentiation between categories. These results suggest that prior exposure can affect processing of a second language in bilingual children, but different mechanisms are used when adapting to different types of speech variation.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".