Variability in L2 Vowel Production: Different Elicitation Methods Affect Individual Speakers Differently
Bibliographic record
Abstract
Elicitation methods are known to influence second language speech production. For teachers and language assessors, awareness of such effects is essential to accurate interpretations of testing outcomes. For speech researchers, understanding why one method gives better performance than another may yield insights into how second-language phonological knowledge is acquired, stored, and retrieved. Given these concerns, this investigation compared L2 vowel intelligibility on two elicitation tasks and determined the degree to which differences generalized across vowels, vowels in context, lexical items, and individual speakers. The dependent variable was the intelligibility of Cantonese speakers' productions of English /i I u ℧/ in varying phonetic environments. In a picture-naming task, the speakers produced responses without an auditory prompt. In a second task-interrupted repetition-they heard exemplars of the same targets without pictures, and repeated each one after counting aloud to 10, a step intended to disrupt their short-term auditory store and therefore prevent simple mimicry. For target words with scores below 80% on picture naming, mean intelligibility was more than 10 points higher on interrupted repetition. However, that difference did not generalize across conditions or across speakers. Thus, although it is technically accurate to say that, on average, interrupted repetition yielded better vowel intelligibility than did picture naming, that observation requires a great deal of qualification, particularly because of individual speaker differences. The outcomes are interpreted in terms of their relevance to language assessment and phonetic learning.
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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 | 0.004 | 0.015 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".