Perception of second-language (L2) production by first-language (L1) speakers of different dialectal backgrounds: The case of Japanese-speaking learners’ /u/ perceived by French and Quebec native speakers.
Bibliographic record
Abstract
The high back rounded /u/ of Parisian French (PF) is characterized by a concentration of energy in the low frequency zone (< 1000 Hz) due to the grouping of the first two formants, while Quebec French (QF) has a “lax” variant [U] in closed syllables (as in “soupe”), with its F2 amounting to 1000–1100 Hz [P. Martin, “Le système vocalique du français du Québec. De l’acoustique à la phonologie.” La linguistique, 38(2), 71–88 (2002)]. Japanese-speaking learners of French (JSL) tend to produce French /u/ with high F2 as in Japanese /u/, which in turn tends to be perceived by PF listeners as /φ/. Do QF listeners show different behavior because of their lax variant of /u/? Our perception experiment using 18 tokens each of /u y φ/ produced by five JSL showed that the 16 PF listeners examined perceived those stimuli of /u/ with F2 between 1000 and 1100 Hz as /u/ and /φ/ almost equally often, but considered as very poor exemplars of either of them. By contrast, the 16 QF listeners tested identified the same stimuli of /u/ almost always as /u/ with a better goodness rating than NF listeners’. These findings suggest that native speakers’ judgment about non-native speakers’ production might depend on the native dialect of the listener.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".