Visual primes, speech intelligibility, and South Asian speech stereotypes
Bibliographic record
Abstract
Visual primes that suggest social attributes about a talker can affect listeners' speech perception. Recent work by Babel and Russell [J. Acoust. Soc. Am. 137 (2015)] showed that speech intelligibility can decrease when listeners are shown a picture of the speaker when the talker is ethnically Chinese than when the talker is not. They reason that listeners associate Chinese faces with nonnative English accents, which hinders intelligibility. This finding has important implications for our understanding of real-world speech intelligibility when talkers' and listeners' race or ethnicity differ and when these are associated with different language backgrounds. Due to ongoing demographic shifts in the US and Canada, interactions between older and younger adults are often between people with different racial, ethnic, and linguistic backgrounds. This project is a part of a larger research collaboration aimed at understanding the extent and nature of effects of race and ethnicity on speech intelligibility across the lifespan and across levels of hearing acuity. In this talk, we will discuss the results of the first of these experiments, in which we examine speech intelligibility in noise using a large set of talkers whose voices are paired with faces of individuals who are White or South Asian.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".