The articulatory dynamics of pre-velar and pre-nasal /æ/-raising in English: An ultrasound study
Bibliographic record
Abstract
Most dialects of North American English exhibit /æ/-raising in some phonological contexts. Both the conditioning environments and the temporal dynamics of the raising vary from region to region. To explore the articulatory basis of /æ/-raising across North American English dialects, acoustic and articulatory data were collected from a regionally diverse group of 24 English speakers from the United States, Canada, and the United Kingdom. A method for examining the temporal dynamics of speech directly from ultrasound video using EigenTongues decomposition [Hueber, Aversano, Chollet, Denby, Dreyfus, Oussar, Roussel, and Stone (2007). in IEEE International Conference on Acoustics, Speech and Signal Processing (Cascadilla, Honolulu, HI)] was applied to extract principal components of filtered images and linear regression to relate articulatory variation to its acoustic consequences. This technique was used to investigate the tongue movements involved in /æ/ production, in order to compare the tongue gestures involved in the various /æ/-raising patterns, and to relate them to their apparent phonetic motivations (nasalization, voicing, and tongue position).
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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".