Asymmetry in French speech-in-noise perception: the effects of native dialect and cross-dialectal exposure
Bibliographic record
Abstract
Abstract Research has shown that speakers’ native production patterns can change after living in adulthood in a region where a second dialect (D2) of their native language is spoken, yet relatively little is known about how speech perception changes after postadolescent D2 exposure. This study explores this topic by examining how varying degrees of exposure to Quebec French and Hexagonal French affect comprehension of speech in these dialects. A speech-in-noise perception experiment was conducted among mobile and nonmobile speakers of Quebec and Hexagonal French to test the competing effects of native dialect and D2 exposure on cross-dialectal speech perception. Results show an own-dialect advantage for all groups in their comprehension of speech in noise, though this advantage is smaller for the mobile groups, particularly for the mobile Hexagonal French listeners. An effect of D2 exposure on D2 perception is also revealed for the mobile Hexagonal listeners but not for the Québécois listeners, indicating an asymmetry in cross-dialectal perception. These findings suggest that, while phonological representations for the native dialect remain robust, processing of D2 speech can improve after extended, postadolescent exposure to this dialect. Furthermore, the extent of this adaptation may be modulated by mobile listeners’ prior experience with 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".