Effects of Prosodic Strengthening on the Production of English High Front Vowels /i, ɪ/ by Native vs. Non-Native Speakers
Bibliographic record
Abstract
This study investigated how acoustic characteristics (i.e., duration, F1, F2) of English high front vowels /i, ɪ/ are modulated by boundary- and prominence-induced strengthening in native vs. non-native (Korean) speech production. The study also examined how the durational difference in vowels due to the voicing of a following consonant (i.e., voiced vs. voiceless) is modified by prosodic strengthening in two different (native vs. non-native) speaker groups. Five native speakers of Canadian English and eight Korean learners of English (intermediate-advanced level) produced 8 minimal pairs with the CVC sequence (e.g., 'beat'-'bit') in varying prosodic contexts. Native speakers distinguished the two vowels in terms of duration, F1, and F2, whereas non-native speakers only showed durational differences. The two groups were similar in that they maximally distinguished the two vowels when the vowels were accented (F2, duration), while neither group showed boundary-induced strengthening in any of the three measurements. The durational differences due to the voicing of the following consonant were also maximized when accented. The results are discussed further in terms of phonetics-prosody interface in L2 production.
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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 | 0.001 |
| 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".