The acoustic counterpart to articulatory resistance and aggressiveness in locus equation metrics and vowel dispersion
Bibliographic record
Abstract
Research on locus equation metrics (LEs) tend to take it for granted that vowel space is invariable across consonantal contexts. However, articulation-based studies report a mutual influence between neighboring segments such that segments with greater constraints in dorsal articulation are more resistant to and concurrently more aggressive in coarticulation than those with less constraints (Farnetani, 1990). We examine (1) whether articulatory resistance and aggressiveness can be acoustically captured through LEs and vowel dispersion and (2) how the relationship between LEs and the degree of coarticulation is mediated by vowel dispersion and delay in voicing. These questions are investigated in CV sequences in English –where C is one of /p t s ʃ/ that varies in the articulatory constraints imposed on the tongue dorsum. We manipulated the magnitude of coarticulation and voicing lag by contrastively stressing the target consonants. Our results show that there is a tight relationship between LE slopes and vowel dispersion where articulatory resistance and aggressiveness appear as the mirror image of each other in the acoustic signal. However, effects of hyperspeech simultaneously affect vowel dispersion, voicing delay, and LEs creating confounds. This calls for caution in the use of LE metrics as a measure of coarticulation.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| 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".