First-language-specific orthographic effects in second-language speech
Bibliographic record
Abstract
We investigated first-language (L1) orthographic effects on second-language (L2) speech production in Korean–English and Farsi–English bilinguals, as compared to English monolinguals. We used a word-reading and word-naming task to compare the production of the single grapheme (letter) (e.g,) with the digraph (e.g.,). An acoustic analysis of 600 tokens in Praat revealed that Korean–English bilinguals exhibited significantly longer [m:] productions compared to English monolinguals, but that the Farsi–English bilinguals did not. Longer/geminate [m:] productions are attributed to orthography-induced L1 transfer. We concluded that orthography does affect L2 word-reading and phonological mental representations, even when the L1 and L2 may have different scripts. We recommend that L2 speech learning be treated as a multi-modal event.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".