Gradient assimilation in French cross-word /<tt>n</tt>/+velar stop sequences
Bibliographic record
Abstract
Articulatory studies have revealed cross-linguistic variation in the realization of cross-word nasal+stop sequences. Whereas languages such as Italian and Spanish show largely categorical regressive place assimilation (Kochetov & Colantoni 2011, Celata et al. 2013), English and German alveolar nasals are often characterized by gradient assimilation, modulated by the degree of overlap with the following gesture (Barry 1991, Ellis & Hardcastle 2002, Jaeger & Hoole 2011). The lack of comparable instrumental studies for French may be due to the common assumption that the language lacks nasal place assimilation in general. We investigate here the production of French /n/+/kɡ/ sequences via electropalatography. Four female speakers of European and Quebecois French wearing custom 62-electrode acrylic palates read the sentencesC'est une bonne casquette‘That's a good cap’ andC'est une bonne galette‘That's a good tart/cookie’ alongside comparable control sentences involving /n/+/t d/ sequences. For each sequence, assimilation type was determined both qualitatively via visual inspection of the linguopalatal profiles and quantitatively using two contact indices. None of the /n/-tokens exhibited either categorical assimilation (i.e. [ŋk]) or lack of assimilation (i.e. [n(ə)k]). Rather, an intermediate pattern was attested with the nasal involving overlapped coronal and velar gestures ([nn͡ŋ]) and continuous retraction of the constriction. The degree of overlap varied among speakers, extending up to half of the nasal interval. Overall, these French patterns are strikingly different from the categorical processes reported for other Romance languages, yet similar to the gradient assimilation attested in Germanic languages. We conclude by discussing possible sources of these differences.
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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 | 0.001 |
| 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 | 0.001 |
| 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".