The effect of physical constraints on articulatory variability in English /r/
Bibliographic record
Abstract
In a recent study of American English /r/, Guenther et al. [J. Acoust. Soc. Am. (1999)] hypothesized that articulatory ‘‘tradeoff’’ correlations are the result of the need to maintain stable acoustic targets. Their findings included (1) a positive correlation between tongue back height and tongue front horizontal position for 7/7 subjects, (2) a negative correlation between tongue back height and tongue front height for 5/7 subjects, and (3) a positive correlation between tongue front horizontal position and tongue front height for 2/7 subjects. The present study investigates the possibility that these correlations result from physical constraints on the tongue such as volume preservation and palate angle. Continuous sentences from the Wisconsin x-ray microbeam database were analyzed to determine whether these same correlations were present across whole utterances presumably lacking a stable F3 target. Results to date show some significant correlations despite extremely high noise levels. Results will be presented for additional measures using vowels only to reduce noise. These initial results suggest that the observed correlations may not result from strict acoustic targets for /r/, but rather from internal and external physical constraints on the tongue. [Work supported by NSERC and NIH.]
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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 | 0.010 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".