Perception in context: How racialized identities impact speech perception
Bibliographic record
Abstract
When available, listeners use visual cues in speech perception [Zheng and Samuel (2017)]. However, it is not clear whether racialized identities impact listeners’ judgments, and if so, to what extent everyday experiences contribute to this. American, British, and Indian English varieties were paired with white and South Asian faces to test whether listeners’ intelligibility and accentedness judgments vary as a function of the faces that they saw and the varieties that they were listening to. A prior norming study was used to assess that sentences in all varieties had similar intelligibility. Listeners in a low-diverse environment (i.e., Gainesville, USA) versus a high-diverse environment (i.e., Montreal, Canada) were recruited. Racial diversity in listeners’ social network and their language diversity [i.e., language entropy, Gullifer and Titone (2020)] were measured. Results showed that listeners’ ability to transcribe sentences (i.e., intelligibility) decreased and their accentedness judgments increased for all English varieties when speech was paired with South Asian faces. Furthermore, these effects were modulated by participants’ social network diversity and their geographic context [Kutlu et al. (2021); (2022)]. We discuss the holistic impacts of the racialization of different language varieties and the role of multilingual and diverse context effects on speech perception.
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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 | 0.004 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".