An acoustic study of [liquid + stop] sequences by native and second-language speakers of English.
Bibliographic record
Abstract
Since the initial work of Mann [Percept. Psychophys. 28, 407–12, (1980)], numerous studies have used liquid + stop clusters in VCCV frames to investigate context effects of liquids on stop perception. However, acoustical studies of related production data have thus far been very limited. The current study will present results of acoustic analyzes of such utterances sampled from 67 native speakers of English (57 from the Canadian Prairie Provinces) and 44 second language speakers of English from a variety of native language backgrounds. The objectives of this work are, first, to understand the nature of variation and covariation of production patterns across a moderate sample of native speakers; second, to investigate how native language background affects production of these sequences (especially those with phonetic realizations of postvocalic liquids that may be quite different from those of English). A key focus will be nature and magnitude of empirical patterns of covariation in relation to perceptual context effects observed in the literature.
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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 | 0.002 |
| 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 | 0.000 | 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 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".