Measuring the effect of speaker ethnicity on online perception: Evidence from a response time study
Bibliographic record
Abstract
The effect of speaker ethnicity on speech perception remains unclear. Proponents of the bias hypothesis maintain that presenting an Asian or Mexican face to American participants triggers a certain kind of bias that could result in worse comprehension and even hearing a non-existent ‘foreign accent.’ Exemplar-based studies, on the other hand, have proposed that these findings merely reflect a mismatch between listeners’ expectations and the actual speech signal. While previous studies all used post-perceptual, offline tasks to examine the effect of speaker ethnicity on speech perception, this study made use of an online task instead. Thirty-two native English participants completed a speeded audio-visual sentence verification task, for which they had to classify statements as true or false. The utterances were paired with a photograph of an Asian face, a White face, or a fixation cross, and were presented in a mixed design. Both correctness scores and response times for all the different face-voice pairings were recorded. Results suggest that online processing was not affected by speaker ethnicity, as response times did not differ as a function of the various face-voice pairings. Additional findings showed that the foreign-accented voices took significantly longer to process than the native voices, and that false statements took longer to answer than true statements.
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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 | 0.005 | 0.031 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".