Is It All or Nothing? The Other Accent Effect in Talker Recognition
Bibliographic record
Abstract
Listeners often have trouble identifying other-accented talkers. Some suggest this Other Accent Effect (OAE) occurs only for non-native accents (e.g., Canadian English listeners experience it for Mandarin-accented English, but not Australian English). But the line between native and non-native accents can be difficult to distinguish, and past studies have confounded accent strength with accent type. Thus, we hypothesize that accent strength modulates the OAE. We predict a heavy non-native accent will elicit an OAE, whereas a light one will not. To test this, we presented native Canadian English listeners with voice line-ups of native Canadian English accented, non-native heavy Mandarin-accented, and non-native light Mandarin-accented talkers. Unsurprisingly, listeners performed better with Canadian English talkers than Mandarin-accented talkers. Crucially, listeners performed equally poorly with both heavy and light Mandarin-accented talkers. Thus, we found no evidence for our hypothesis; instead, we observe that even a weak non-native accent can elicit a strong OAE.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".