The Emergence of the Other Accent Effect in Talker Recognition
Bibliographic record
Abstract
Adults are more accurate at identifying own- than other-accented talkers (e.g., Stevenage, 2012). However, no study to date has examined when the Other Accent Effect (OAE) first emerges. Here, we use a voice line-up task to test 6- to 12-year-olds (N=59) on their ability to recognize own- (Canadian English) and other-accented (Japanese- or Mandarin-accented English) talkers. Own-accented talkers (M=0.77) were recognized better than other-accented talkers (M=0.61), p<.005. No effect of age was observed. This indicates the OAE is well-entrenched by 6 years of age. Interestingly, all children tended to evaluate other-accented talkers negatively, but these biases did not predict the strength of the OAE, suggesting that a factor other than social biases drives the effect (Yu et al., 2021). To further understand the underlying mechanisms of the OAE, we are currently testing children in the same line-up task on their ability to recognize Southern American and Australian English talkers.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".