Role of first language dialect in the production of second language German vowels
Bibliographic record
Abstract
A methodological shortcoming in previous second language (L2) acquisition studies has been that researchers have assumed an overly homogenous first language (L1) ignoring dialect differences. In the current study English and German vowel production data were collected from 72 English-speaking learners of German from three distinct North American English dialect regions – the Inland North, North Central, and Western Canada. Following Flege (e.g., 1995), who proposed that L2 segments with L1 counterparts would be more difficult to perceive and produce than new L2 segments, we show that subjects did not transfer their L1 /u/ to German but rather produced the German counterpart in a manner expected in neither German nor English. Instead, this was reflected in terms of formant (F1, F2 or F3) values that varied according to the L1 dialect of the learner. In particular, learners from the North Central dialect region whose English /u/ was produced with the lowest F2 values – though not significantly different from the Inland North learners' /u/ – produced the German /u:/ with the highest F2 values of all three dialect regions. Speakers from all dialect regions were also able to manipulate their acoustic space to allow for the addition of the new German segment /y:/; however, they differed in how they ultimately established the German /u:/–/y:/ contrast. Learners from the two American dialect regions contrasted these vowels according to F2 values, while learners from Western Canada made the contrast utilizing F3. Based on these results, we conclude that L2 vowel formant values differ by dialect region even when the learners' L1 dialects differ only subtly. Lastly, results provide further evidence that this influence is not simply the result of direct L1 transfer.
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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 | 0.000 |
| 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 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".