Relative Difficulty in the Acquisition of the Phonetic Parameters of Obstruent Coda Voicing: Evidence from Mandarin-Speaking Learners of French
Bibliographic record
Abstract
A recurring finding of research on the L2 acquisition of coda obstruent voicing is that, in terms of the phonetic parameters that serve to realize the voicing contrast, learners are overwhelmingly more accurate with duration than the voicing of the obstruent itself. The current work expands our understanding of this asymmetry in two ways. First, as previous studies have focused almost exclusively on learners of English, we investigate here whether L2 learners' superior production of duration is also found among learners of other target languages via a study of Mandarin-speaking learners' production of French stop and fricative codas. Results from 18 Mandarin-speaking learners of French, primarily of beginner and intermediate proficiency who completed a sentence reading task, parallel those of previous studies with greater accuracy observed for vowel duration than the laryngeal voicing of the obstruent. Second, we explore potential sources of this asymmetry, in particular, the roles of L1 experience as well as of universal factors, namely, the relative perceptual salience of duration versus voicing, and the articulatory difficulty of voicing obstruents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".