Investigating the influence of listener attitudes and expectations on the intelligibility of Hindi-influenced English
Bibliographic record
Abstract
Phoneme recognition and speech intelligibility can be affected by purely social factors, such as beliefs about or attitudes toward speakers of varying races and ethnicities (e.g., Babel & Russell, J. Acoust. Soc. Am. 137 [2015]). The current study examined whether implicit and explicit attitudes about people from South Asian (SA) affect the intelligibility of Hindi-influenced English (HIE). Three measures were used: an Implicit Association Test (IAT) to measure general associative attitudes toward SA individuals; an intelligibility task in which listeners repeated HIE and North American English (NAE) sentences in noise, accompanied by pictures of SA or white individuals; and an ethnographic interview to acquire qualitative data about stereotypes, communicative practices with nonnative speakers, and other language attitudes. Data collection is ongoing in a more ethnically diverse location (Vancouver, BC) and a more homogenous one (Minneapolis, MN). Drawing from past studies on socioindexical influences on nonnative speech, we predict that individuals who harbor negative attitudes and stereotypes about SA people will perceive HIE as less intelligible than those without such beliefs. The results of this study will inform the growing body of literature regarding the ways in which socioindexical factors influence speech recognition across individuals with diverse ethnicities and language varieties.
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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 | 0.003 | 0.012 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".