Lexical frequency, orthographic information, and first-language effects on second-language pronunciation.
Bibliographic record
Abstract
In L2 speech learning, lexical frequency may play a facilitative role, whereby perception and production of sounds found in high-frequency lexical items will develop before the perception and production of the same categories found in low-frequency lexical items (see Munro and Derwing, 2008). Orthographic information may also facilitate learning by disambiguating L2 sounds (see Erdener and Burnham, 2005), particularly in known words. This study examines the role of lexical frequency, orthographic information, and a learner’s L1 in the development of L2 speech perception and production. Thirty-eight Mandarin and Slavic participants were asked to repeat a word list comprising ten target English vowels, each embedded in three separate monosyllabic verbs and varying in lexical frequency. Recordings of the L2 productions were obtained under three counter-balanced conditions: (1) after hearing an auditory prompt accompanied by the written form of the word; (2) after hearing an auditory prompt with no written form provided; and (3) with no auditory prompt but the written form provided. To measure L2 performance, L1 English listeners were asked to identify the vowel they perceived in each recorded item. Results were examined to determine what influence lexical frequency and orthographic information might have had on L2 performance.
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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 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".