The Acquisition of /ɪ/–/iː/ Is Challenging: Perceptual and Production Evidence from Cypriot Greek Speakers of English
Bibliographic record
Abstract
This study aims to investigate the perception and production of the English /ɪ/-/iː/ vowel contrast by Cypriot Greek speakers of English as a second language (L2). The participants completed a classification test in which they classified the L2 vowels in terms of their first language (L1) categories, a discrimination test in which they distinguished the members of the vowel contrast, and a production test in which they produced the target vowels. The results showed that they classified both L2 /ɪ/-/iː/ mostly in terms of L1 /i/, which denotes the formation of a completely overlapping contrast according to the theoretical framework of the Universal Perceptual Model (UPM), and that they could hardly distinguish the vowel pair. In addition, their productions deviated in most acoustic parameters from the corresponding productions of English controls. The findings suggest that /ɪ/-/iː/ may carry a universal marker of difficulty for speakers with L1s that do not possess this contrast. This distinction is difficult even for experienced L2 speakers probably because they had never been exposed to naturalistic L2 stimuli and they do not use the L2 that much in their daily life. Finally, the study verifies UPM's predictions about the discriminability of the contrast and extends the model's implications to speech production; when an L2 vowel contrast is perceived as completely overlapping, speakers activate a (near-) unified interlinguistic exemplar in their vowel space, which represents both L2 vowels.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".