Second language perception of English vowels by Portuguese learners: The effect of stimulus type
Bibliographic record
Abstract
The present study investigated L2 English vowel perception and the effect of stimulus type on the identification of vowel segments that present difficulties for Portuguese learners. It also examined the effect of subject factors such as age of acquisition, length of formal instruction, language use and vocabulary size, on the L2 learners’ perceptual performance. Twenty-nine adult Portuguese learners of English were tested on six English vowels (/iː ɪ ɛ æ ɜː ʌ/) with two tasks, differing in stimulus type, i.e., in the lexical status of trials (real words and pseudo words) in which the target vowels were auditorily presented. The testing stimuli consisted of 72 trials with real CVC words and 72 trials with pseudo CVC words, naturally produced by two speakers of standard southern British English (SSBE). The L2 vocabulary size of the participants was measured with two receptive vocabulary size tests and the language background data, viz. age of learning, length of formal instruction and L2 use was collected with a questionnaire. Results confirmed the Portuguese learners’ difficulties in accurately categorizing the target vowels, particularly when identifying the vowel target sounds embedded in pseudo words, which suggests that L2 phonological categories may be established after lexical forms. Furthermore, a significant correlation was found between L2 language use and accurate perception of four of the target vowels, which indicates that the more frequently learners use the target language, the more accurate is their L2 English vowel perception.
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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 | 0.009 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".