Instrumental Analysis of English Vowels Produced by Male and Female Zilfaawi Arabic Speakers
Bibliographic record
Abstract
Arab and non-Arab English as a foreign language (EFL) students continue to have difficulty pronouncing English vowels accurately. To examine this, our study analyzes how male and female Saudi EFL students pronounce English monophthongs when compared to native speakers assessed in previous research. Gender-related variations between male and female Arab English speakers are also explored. Formant frequencies (F1 and F2) are employed to evaluate vowel quality, with vowel duration measured to investigate vowel length. Learners’ pronunciations of English words containing vowels of interest are used to collect data. Five male and five female EFL learners produced English monophthongs in the /hVd/ context. We then compare the results with previous data on native English speakers and conduct acoustic analysis. Regarding duration, male non-native English speakers’ data are compared with previous results for male native speakers, revealing that the vowels of Saudi learners are shorter than those of native English speakers, and those of non-native men are longer than those of non-native women. Moreover, the low vowels produced by Saudi and native men are longer than their non-low vowels. Regarding vowel quality, men produce lower vowels than native speakers. Women, however, produce lower and more front vowels than native women. Statistically, this study reveals significant differences between male and female Saudi EFL learners in producing English vowels. Saudi men’s vowel space is more centralized than Saudi women’s space. Both men and women overlap low vowels. Saudi learners’ mispronunciations of English vowels indicate that L1 interference is not the only cause of mispronunciations.
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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 | 0.001 |
| 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) | 0.003 | 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".