Duration imitation is not mediated by phonological contrast: Evidence from a checked-unchecked tonal contrast in Taiwanese Southern Min
Bibliographic record
Abstract
Phonetic imitation is mediated by phonological contrast, as evident in features such as formant, VOT and F0. However, a recent study observed that duration imitation was not mediated by phonological contrast. In contrast to other studies, duration served as a non-primary cue to the phonological contrast in this recent study. This current study further investigates duration imitation in a case where duration serves as the primary cue. We utilized the tonal contrast of T3 versus T33 in Taiwanese Southern Min (TSM), to which duration was identified as the primary cue. We created a seven-step tonal continuum between T3 and T33 by manipulating the tone durations, and recruited seventeen native TSM speakers to imitate each step as closely as they could. The bi- or uni-modality of the distribution of the imitated durations for all seven steps was analyzed using Bayesian regression models. Results showed that the imitated durations were more consistent with an unimodal distribution, suggesting that, unlike other features, the imitation of duration is not mediated by tonal contrast, whether it acts as a primary cue or not. Thus, features exhibit different resistance to phonological mediation in phonetic imitation.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".