Coarticulation across word boundaries and its role in L2 English accentedness
Bibliographic record
Abstract
L2 learners gradually acquire accurate segmental production and appropriate word boundary coarticulation.Even when segmental accuracy is high, differences in interword articulation may still influence perceived accentedness.This pilot study tested a protocol for examining whether word-boundary consonant errors and speech rate relate to perceived accentedness in L2 English.Six adult L2 speakers (L1s: Mandarin, Thai, Finnish) and one native control completed a phrase-repetition task and read two longer passages aloud.Word-boundary consonant errors were identified using combined audio and lingualultrasound analysis.Two expert listeners independently ranked speakers by accentedness, and speech rate was measured relative to a native model.Strong positive correlations emerged between accentedness and both the frequency of word-boundary errors (r = .96)and overall speech duration (r = .95).Highly accented speech showed coda omissions, voicing errors, assimilations, and increased variability across repetitions, particularly in clusters and stops with shared places of articulation.Slower speech with hesitations at word boundaries also aligned with higher accentedness ratings.These findings suggest that word-boundary articulation and speech rate may offer useful objective indicators of L2 pronunciation proficiency and highlight the instructional value of targeting coarticulation and linking words to phrases.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".