When inferring lingual gestures from acoustic data goes wrong: The case of high vowels in Canadian French
Bibliographic record
Abstract
Canadian French (CF) is distinguished from other dialects partly by the presence of lax high vowel allophones in closed syllables (Walker, 1984). Acoustically, tense high vowels are characterized by a lower F1 than their lax counterparts, which could be the result of tongue root advancement, tongue body raising, or both (Ladefoged & Maddieson, 1996). High vowel allophony in CF therefore represents a case in which articulatory gestures cannot reliably be inferred from acoustic data alone. Nevertheless, the literature discussing the phonetic properties of high vowels in CF commonly assumes tongue root position to be the parameter that distinguishes between tense and lax vowels, despite an absence of empirical evidence. The purpose of this experiment is to test this assumption and provide articulatory evidence using ultrasound imaging to examine tongue position during speech production by CF speakers. Results indicate that an advanced tongue root gesture is not used to distinguish between high vowels in CF; no significant difference in tongue root position was found between tense and lax allophones. Rather, tongue body height was found to be the distinguishing feature. These findings contribute to our knowledge of the typology of articulatory gestures used to distinguish between so-called tense and lax vowels.
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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 | 0.011 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.003 |
| 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".