Does race impact speech perception? An account of accented speech in two different multilingual locales
Bibliographic record
Abstract
Upon hearing someone's speech, a listener can access information such as the speaker's age, gender identity, socioeconomic status, and their linguistic background. However, an open question is whether living in different locales modulates how listeners use these factors to assess speakers' speech. Here, an audio-visual test was used to measure whether listeners' accentedness judgments and intelligibility (i.e., speech perception) can be modulated depending on racial information in faces that they see. American, British, and Indian English were used as three different English varieties of speech. These speech samples were presented with either a white female face or a South Asian female face. Two experiments were completed in two locales: Gainesville, Florida (USA) and Montreal, Quebec (Canada). Overall, Montreal listeners were more accurate in their transcription of sentences (i.e., intelligibility) compared to Gainesville listeners. Moreover, Gainesville listeners' ability to transcribe the same spoken sentences decreased for all varieties when listening to speech paired with South Asian faces. However, seeing a white or a South Asian face did not impact speech intelligibility for the same spoken sentences for Montreal listeners. Finally, listeners' accentedness judgments increased for American English and Indian English when the visual information changed from a white face to a South Asian face in Gainesville, but not in Montreal. These findings suggest that visual cues for race impact speech perception to a greater degree in locales with greater ecological diversity.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".