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 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 | 0.002 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".