Locus equation metrics as an index of coarticulation resistance: The effect of prosodic prominence
Bibliographic record
Abstract
The degree of articulatory constraints (DAC) model (Recasens, Pallarès, & Fontdevila, 1997) proposes that consonants involving movement of the tongue dorsum are more resistant to coarticulation than consonants that have a less constrained articulation. The present study aims to assess this claim using locus equation (LE) slopes as indicators of coarticulation. Participants were asked to produce V1.C1V2 sequences as part of two-word phrases in a scripted dialogue, where C1 is one of /p, t, s, ∫/. LE were derived by measuring F2 at V2 onset and midpoint. Given the DAC model’s predictions, and given also that steeper LE slopes indicate higher levels of coarticulation, it was predicted that segments’ LE slopes would rank from steepest to shallowest as /p/>/t/>/s/>/∫/. Results were highly consistent with these hypotheses, lending support to the DAC model. A secondary hypothesis predicted that contrastively stressing C1 would reduce coarticulation, resulting in shallower LE slopes relative to a control condition (unstressed C1). We found that the LE slopes of sibilants /s/ and /∫/, but not stops /p/ and /t/, flattened under prominence as hypothesized. Possible interpretations of this finding are discussed.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".