Do I need to repeat myself? Getting to the root of the Other Accent Effect
Bibliographic record
Abstract
Listeners struggle to identify talkers with a different accent than their own, a phenomenon known as the Other Accent Effect (OAE). But for reasons that are not well understood, the OAE is not consistently observed in all studies. Comprehension-related processing demands offer one explanation, such that other-accented talkers who are more easily understood are also easier to recognize. Here, we test this hypothesis using a forensic-style voice line-up. We examine native English-speaking adults’ ability to recognize talkers from four accent groups, manipulating comprehension-related processing demands by presenting listeners with predictable sentences and subtitles (low-demand condition), or variable sentences without subtitles (high-demand condition). As predicted, the OAE was only observed for talkers with non-native accents. But crucially, our comprehension manipulation had no impact on talker recognition accuracy of any accent type. We conclude that comprehension ease is likely not a key factor driving the OAE. Other possible explanations are discussed.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".