Location and size of constriction in labiovelar and velar sounds in English
Bibliographic record
Abstract
Not all velar sounds are produced using a velar constriction. Previous research [Islam et al., 2024, JASA 155] reveals both velar and palato-velar constriction locations in Parisian French velars. The current study investigates whether this variation is language-specific by comparing the size and location of constriction of velar sounds in English using an rtMRI speech corpus [Lim et al., 2021, Scientific Data 8]. Using Whispgrid [Martin, 2024, Github] and Montreal Forced Aligner [McAuliffe et al., 2017, Interspeech], Praat [Boersma & Weenink, 2024] textgrids were auto-generated and manually corrected in sentence and phone tiers to extract MRi freeze-frames from 10 participants split evenly between (5) L1 and (5) L2 English speakers. A Python script measuring Euclidean Distance located the size and constriction of English velar and labiovelar sounds between the palate and tongue. Results from MRI freeze-frames suggest that in English L1 and L2 speech, velar stops maintain a velar constriction and are not fronted across vocalic contexts found in French velar stops. Variation for velars indicates language-specific trends in constriction location.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".