Toddlers’ Word Recognition in an Unfamiliar Regional Accent: The Role of Local Sentence Context and Prior Accent Exposure
Bibliographic record
Abstract
Adults are generally adept at recognizing familiar words in unfamiliar accents. However, studies testing young children’s abilities to cope with accent-related variation in the speech signal have generated mixed results, with some work emphasizing toddlers’ early competence and other work focusing more on their long-lasting difficulties in this domain. Here, we set out to unify these two perspectives and propose that task demands may play a crucial role in children’s recognition of accented words. To this end, Canadian-English-learning 28-month-olds’ looks to images on a screen were recorded while they were presented with a Scottish-accented speaker instructing them to find a depicted target object. To examine the effect of task demands, both local sentence context and prior accent exposure were manipulated. Overall, Canadian toddlers were found to recognize Scottish-accented words successfully, showing above-chance performance in the identification of words produced in an unfamiliar accent, even when target labels were presented in isolation. However, word recognition was considerably more robust when target words were presented in sentence context. Prior exposure to the unfamiliar Scottish accent in the laboratory did not modulate children’s performance in this task. Taken together, these findings suggest that at least some task-related factors can affect children’s recognition of accented words. Understanding unfamiliar accents, like understanding familiar accents, is thus not an isolated skill but, rather, is susceptible to contextual circumstances. Future models of spoken language processing in toddlerhood should incorporate these early effects of task demands.
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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 | 0.000 |
| 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".