Comparing Phonetic Convergence in Children and Adults
Bibliographic record
Abstract
Observations by sociolinguists suggest that when children relocate to a new community, they rapidly learn to imitate their peers, adopting the new local accent faster and more effectively than adults. However, few well-controlled laboratory experiments have been conducted comparing speech or accent imitation across ages. Here, we investigated Canadian English-speaking children's and adults' imitation of three model speakers: a Canadian English talker, an Australian English talker, and a non-native Mandarin English talker who learned English later in life. The speech of all three talkers was manipulated to have elongated voice onset time (VOT) on word initial stop consonants. The dependent measure was how much participants would lengthen their VOTs after exposure to one of the talkers in two paradigms: delayed shadowing (Experiment 1) and immediate shadowing (Experiment 2). We predicted that overall children would show more imitation than adults, particularly when imitating the Canadian English talker, given previous work on children's social preferences. Although we did not observe age differences in either study, when shadowing was immediate, we found that imitation was influenced by the accent of the speaker, but not in the manner we predicted: both age groups imitated the Mandarin-accented model more strongly than the Canadian model. When shadowing was delayed, we observed no evidence of imitation. We discuss our findings in light of other recent work, and conclude that the development of speech imitation is an area ripe for further investigation.
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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 | 0.001 | 0.001 |
| 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) | 0.004 | 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".