Modulation of phonetic duration by morphological and lexical predictors.
Bibliographic record
Abstract
This study investigates how the duration of the stem vowel of regular and irregular English verbs is modulated by tense (present and past), regularity, lexical frequency, gang size of the vocalic alternation, imageability ratings, and vowel quality. The vocalic durations of 48 monosyllabic irregular verbs and 171 regular verbs were extracted from the Buckeye Corpus of spontaneous speech. A linear mixed effects regression model revealed that vowels of past tense forms tend to have longer durations than vowels of present tense forms, that vowels of words that are less imageable are realized with shorter durations, and that tense vowels are longer than lax vowels. Surprisingly, higher frequency irregular past tense forms were produced with longer vowels, contradicting Aylett and Turk [(2004); (2006)] and Bell et al. [(2003); (2009)]. Further, vowels were shorter for irregular verbs with larger vocalic alternation gangs, contradicting the predictions of Kuperman et al. [(2007)] but supporting hypotheses that units with a smaller information load have shorter durations. This pattern of results is interpreted as a consequence of the pressure for regularization during the production of irregular past tense forms.
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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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".