Coarticulation and assimilation in Korean vowel epenthesis
Bibliographic record
Abstract
This paper investigates acoustic properties of epenthetic vowels used in the adaptation of English loanwords with final obstruents in Korean (e.g. poki < folk, poci < poach). An extensive analysis of spectral and durational properties of these vowels produced by six speakers of Seoul Korean reveals that loanword epenthesis is a categorical vowel insertion process. The quality of epenthetic vowels in the data was essentially identical to that of the native high vowels /i/ and /i/, depending on the place of articulation of the preceding consonant. Duration of epenthetic vowels was also similar to that of native vowels. These findings provide evidence for the phonological status of epenthesis and vowel coloring in loanwords into Korean, supporting some previous phonological accounts of the phenomenon, while questioning others. Importantly, the categorical vowel coloring in loanwords is different from gradient coarticulatory effects exerted by preceding consonants and non-adjacent vowels, which were also observed in the data. This underscores the importance of careful experimental investigation of vowel epenthesis, as a way of teasing apart phonological processes and phonetic effects. 2010 Elsevier B.V. All rights reserved. * Corresponding author. Tel.: +1 607 262 5727; fax: +1 416 971 2688. E-mail addresses: kyumin.kim@utoronto.ca (K. Kim), al.kochetov@utoronto.ca (A. Kochetov).
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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 | 0.001 |
| 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".