Bibliographic record
Abstract
Previous work has been found to be surprisingly low within-speaker variability in baseline articulator positions during inter-utterance nonspeech [Gick, Phonetica (2002)], raising the question of whether these baseline positions may in fact be active in speech production. If so, then they should be specified and should vary systematically across languages. A study was conducted to test for cross-language differences in inter-utterance articulator positions. Individual video frames were extracted at the midpoint of interutterance pauses in x-ray films of 5 French and 5 English speakers. Measures were made of articulator positions relative to fixed bone points, and values normalized to jaw size. Frames with potentially confounding surrounding phonetic contexts were omitted. Results for lip measures indicate that French speakers have significantly greater protrusion of the lower lip, but significantly less upper lip protrusion, than English speakers. Additional results will be presented for lingual articulators. Thus these baseline vocal tract configurations do appear to be specified differently for different languages. Additional implications will be discussed, such as possible roles these configurations may play in phonology, potential influence on vowel systems (especially schwa), and cross-language vowel normalization. [Research supported by NSERC and NIH.]
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".