Do I need to repeat myself? Getting to the root of the Other Accent Effect
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 it