The Impact of Race on Speech Perception and Accentedness Judgements in Racially Diverse and Non-diverse Groups
Bibliographic record
Abstract
Abstract Standard varieties are often perceived as morally superior compared with nonstandard varieties (Hill 2008). Consequently, these differences lead to ideologies that racialize nonstandard varieties (Rosa 2016), and increase the negative stereotypes towards nonstandard varieties (Giles and Watson 2013). One outlet of such stereotypes can be observed with speech intelligibility and accentedness judgements. This study examines whether seeing a White or a South Asian face impacts listeners’ perception of American, British, and Indian English and to what extent listeners’ social network diversity plays a role in predicting their perception of speech. Results indicated that intelligibility scores decreased and accentedness judgements increased for all varieties when speech was paired with South Asian faces. However, listeners with less racially diverse social networks had the highest accentedness judgements. Understanding how to account for the emergence and behavioral implications of different English varieties is a pressing question, and these results shed light on how English varieties are perceived. The implications will be discussed in light of language teaching, linguistic practices, and language research.
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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 | 0.001 | 0.006 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".