Perception of French speech-in-noise: the effects of native background and second dialect exposure
Bibliographic record
Abstract
While research has shown that native production patterns can change after living in a region where a second dialect (D2) of their native language is spoken, relatively little is known about how speech perception changes after D2 exposure in adulthood. This study explores this topic by examining how varying degrees of exposure to Quebec French (QF) and Hexagonal French (HF, i.e. French of continental France) affect comprehension of speech in these dialects. To test the competing effects of native dialect and D2 exposure, a cross-dialectal speech-in-noise (SIN) perception experiment was conducted among both mobile and non-mobile speakers of QF and HF. Results show an own-dialect advantage for all groups in the perception of QF and HF SIN. However, this advantage is smaller for the mobile groups, and particularly for the mobile HF listeners. While results indicate perceptual adaptation among both mobile groups due to D2 exposure, stronger evidence for such an effect is found for the mobile HF vs. mobile QF listeners. Generally, these findings suggest that, while perceptual change can occur due to extended, post-adolescent D2 exposure, the extent of such change is mediated by the global prestige of a dialect and, relatedly, listeners' past exposure to this dialect.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".