The Perception in Saudi Learners of the English Bilabial Stops and the English Labio-Dental Fricatives
Bibliographic record
Abstract
Learning to produce and to identify sounds (phonemes) is not the same as learning the difference between sounds which leads to meaning delivery. One part of the acquisition of phonetics is the ability to perceive sounds which distinguish differences in meaning. This paper explores the perception in Saudi learners of the English Bilabial Stops /p/ and /b/ and the English Labio-dental Fricatives /f/ and /v/. Four different groups took part in this experiment. These groups were divided according to their age and their exposure to English either in English speaking countries or elsewhere. The participants had to listen to the different phonemes occurring initially, medially and finally. One of these groups of words contained non-sense words to test the participants’ mis-perceptions when they do not recognize the sounds as part of their mental lexical knowledge. The results show these four groups faced difficulties perceiving and recognizing some sounds according to their exposure to English. Two groups, consisting of adults and children, showed very few misperceptions and/or missed sounds because they studied English in Australia for more than three years. Children had better perception than adults. The other groups show to had more misperceptions and/or missed sounds. Participants who had studied English in Australia for less than six months showed fewer misperceptions than those participants who had studied English in Saudi Arabia. This study suggests that teaching articulation (pronunciation) to Saudi learners of English in early stages is essential in order to avoid unconscious miscommunications due to the wrong perception and production of phonemes.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".