Morphological effects on formant movement in spontaneous speech
Bibliographic record
Abstract
In the present study we investigate the role of morphology in the production of vowel formant movement and centralization. Our data consist of 74 monosyllabic irregular English verbs (6028 tokens) that differ between their present/past tense forms on a single vowel (e.g., sing/sang). For each vowel, we measured F1 and F2 contours and Euclidean distances from speakers’ vowel space centres (measured at the vowel midpoint). A generalized additive model of the formant trajectories and a linear mixed effects regression model of the vowel centralization distances were created comparing two morphological predictors: verb tense (past or present), and paradigmatic support for the vowel. Paradigmatic support was measured using naïve discriminative learning as a metric which determined the association strength between a vowel and tense. A strong association with the past tense is evidence for greater paradigmatic support (i.e., it is more discriminating) while a vowel strongly associated with the present tense has low support. Our results indicate that the morphological predictors have an overall effect on formant movement (both in the past tense and with low paradigmatic support) and vowel centralization. However, the morphological effects differ between individual vowels.
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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 | 0.012 |
| 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 | 0.001 | 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".