A phonetic-phonological study of vowel height and nasal coarticulation in French
Bibliographic record
Abstract
ABSTRACT The majority of previous studies on nasal coarticulation in French find an inversely proportionate relationship between vowel opening and nasality, such that high vowels are the most nasalized, sometimes exceeding 50% nasality. However, it has been unclear whether this is a mechanical or controlled property of French, given the typically short duration of high vowels in natural speech, as well as the aerodynamic and acoustic factors rendering them more susceptible to spontaneous nasalization. This study uses nasometric data to quantify progressive and regressive nasalization in 20 Northern Metropolitan French speakers as a function of vowel height. Furthermore, the relationship between degree of nasal coupling and overall vowel duration serves as a proxy for distinguishing mechanical from controlled nasalization, in the spirit of Solé (1992, 2007). This study finds evidence that high vowel nasalization in French is mechanical in pre-nasal position, but controlled in post-nasal position. Meanwhile, nasalization of mid and low vowels is blocked in pre-nasal position but, at most, mechanical in post-nasal position. In consequence, French appears to block nasalization in otherwise lexically impossible positions (*ṼN), while passively allowing, though not actively requiring, nasalizing in positions where conflation is possible (both NṼ and NV being permitted in the lexicon).
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".