Navigating Accent Variation: A Developmental Perspective
Bibliographic record
Abstract
Adult processing of other-accented speech is fast, dependent on lexical access, and readily generalizable to new words. But what does children's processing of other-accented speech look like? Although many acquisition researchers have emphasized how other-accented speech presents a formidable challenge to young children, we argue that the field has perhaps underestimated children's early accent processing abilities. In support of this view, we present evidence that 2-year-olds’ accent processing abilities appear to be in many respects adult-like, and discuss the growing literature on children's ability to cope with multi-accent input in the natural world. We outline different theoretical outlooks on the transition children make from infancy to later childhood, and discuss how the growing sophistication of infants’ accent processing abilities feeds into their social perception of the world (and perhaps vice versa). We also argue that efficient processing and meaningful interpretation of accent variation are fundamental to human cognition, and that early proficiency with accent variation (along with all of the implied representational and learning capacities) is difficult to explain without assuming the early emergence of abstract speech representations.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".