An acoustic examination of English fricative production by Korean- and Farsi-English bilinguals: The role of language- and orthographic-specific effects
Bibliographic record
Abstract
Recent work has shown that exposure to orthographic effects can promote first-language (L1) phonological transfer. However, it is relatively unknown whether orthographic effects persist in highly proficient bilinguals. Here, we examined how L1 orthographic depth (regularity in grapheme-phoneme correspondences) modulated Korean-English and Farsi-English bilinguals’ (n = 25 each) production of fricatives in English words (e.g., /ˈmeloʊ/ vs. /ˈmelən/). Native English speakers (n = 25) served as a control group. Because fricatives are produced as geminate sounds in both Korean and Farsi, we expected L1-based transfer. Participants completed four tasks: an eye-movement reading task, word-naming task, cloze test, and language background questionnaire. The stimuli were controlled for word frequency, word length, number of syllables, and stress. We expect language-specific differences, corroborating previous neuro-linguistic evidence that shallow and deep orthographies differentially rely more heavily on phonological and lexical pathways, depending on language-specific demands. To explore this aspect, we employed an acoustic classification method for fricatives extracting cepstral coefficients and using HMMs to divide the sounds in regions based on their internal variance, aimed at determining whether fricatives produced by different groups can be classified correctly. This work has implications for both second-language (L2) speech learning models and classroom instruction.
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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) | 0.002 | 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".