Underpinnings of explicit phonetic imitation: perception, production, and variability
Bibliographic record
Abstract
This work tests the relative role of perception- and production-based predictors, and the relationship between them, in imitation of artificial accents varying in voice onset time (VOT), using a paradigm designed to target distinct sub-processes of imitation. We examined how explicit imitation of sentences differing systematically in voice onset time (VOT) was influenced by the type of VOT manipulation (lengthened vs. shortened) and by the presence vs. absence of voice-related variability in exposure. In contrast to previous work, participants imitated shortened as well as lengthened VOT, albeit with both qualitative and quantitative differences across the two manipulation types. The presence of voice-related variability inhibited imitation, but this inhibition was mitigated by a preceding session with no voice-related variability (i.e., sentences were acoustically identical except for VOT). We then tested the extent to which individual performance on the accent imitation task was related to performance on three other tasks: 1) discrimination of the target accents, 2) imitation of words in isolation drawn from a VOT continuum, and 3) discrimination of these same words. Performance on the accent discrimination task and the word-level imitation task, but not the word-level discrimination task, were independently predictive of accent imitation. Results are consistent with a conceptualization of explicit imitation as the sum of automatic phonetic convergence processes overlaid with distinct, controlled perceptual and articulatory factors that pattern differently across individuals. Phonetic imitation should not be considered as a monolithic skill, and models predicting variation in imitative ability must consider not only the potential sources of individual variability, but also at what level these sources of variability exert their influence. 
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".