A comparative acoustic analysis of Korean vowels by native and non-native speakers
Bibliographic record
Abstract
The primary purpose of this study is to examine L1 transfer in L2 vowel acquisition (Flege 1995,1996) by comparing L1 vowels with L2 Korean vowels produced by Korean native speakers and both Mandarin and English learners of Korean. It would predict which L2 Korean vowels are relatively difficulty or easy to produce for the non-native speakers based on acoustic similarities and dissimilarities between their L1 vowels and L2 Korean vowels. A total of 68 female speakers participated in a word-list reading task. For acoustic analysis, the formant frequencies (F1 and F2) and vowel duration were measured. Results demonstrated that there were cross-language differences in both vowel quality and duration. Both Mandarin and English learners of Korean perform well when producing Korean [a, i] vowels, but have difficulty producing Korean vowel contrasts [ʌ]-[o], [o]-[u], [ɨ]-[u]. In terms of vowel duration, Korean vowels were the shortest, English vowels were the longest, and Mandarin vowels were intermediate between the two. Overall, the L2 Korean vowel duration of both Mandarin and English speakers was too short, compared to their L1 vowel productions, to have a Korean native-like performance of vowels.
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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 | 0.002 |
| 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 | 0.000 |
| 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".