Segmenting words from fluent speech during infancy – challenges and opportunities in a bilingual context
Bibliographic record
Abstract
Previous research shows that word segmentation is a language-specific skill. Here, we tested segmentation of bi-syllabic words in two languages (French; English) within the same infants in a single test session. In Experiment 1, monolingual 8-month-olds (French; English) segmented bi-syllabic words in their native language, but not in an unfamiliar and rhythmically different language. In Experiment 2, bilingual infants acquiring French and English demonstrated successful segmentation for French when it was tested first, but not for English and not for either language when tested second. There were no effects of language exposure on this pattern of findings. In Experiment 3, bilingual infants segmented the same English materials used in Experiment 2 when they were tested using the standard segmentation procedure, which provided more exposure to the test stimuli. These findings show that segmenting words in both their native languages in the dual-language task poses a distinct challenge for bilingual 8-month-olds acquiring French and English. Further research exploring early word segmentation will advance our understanding of bilingual acquisition and expand our fundamental knowledge of language and cognitive development.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".