Using ultrasound to examine contrastive hyperarticulation
Bibliographic record
Abstract
The current paper examines the extent to which there is cross-linguistic evidence for the hyperarticulation of more phonologically contrastive vowel sounds. Hall et al. [2017, JCAA 45: 15] found that tense vowels in English are produced with more total tongue movement when in positions where they are more phonologically contrastive. This finding was established through the use of Optical Flow Analysis [Horn & Schunck 1981] on ultrasound videos of the tongue. The “more contrastive” positions were those in which the vowels could contrast with their lax vowel counterparts, while the “less contrastive” positions were those in which no such contrast was possible. While there was evidence that the degree of contrast affected the tongue movements, the data were somewhat confounded by the fact that the more contrastive positions were largely closed syllables, while the less contrastive positions were largely open syllables. In the current paper, we first replicate the original results with more tightly controlled phonetic contexts in English and then examine analogous results for Canadian French. Crucially, in Canadian French, [e] vs. [ɛ] contrast in open syllables and not in closed, such that the effects of syllable position and phonological contrast can be teased apart. [Funded by SSHRC.]
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".