"I mean <i>ship</i> not <i>sheep</i> ": Intelligibility-based phonetic adaptation in native-nonnative conversation
Bibliographic record
Abstract
During native-nonnative interactions, speakers often need to adjust their speech to promote understanding. This study investigates how native and nonnative speakers adapt their speech during real-time conversation to resolve miscommunications stemming from phonemic confusions between English tense- and lax-vowel words (e.g., sheep versus ship). Native English-nonnative (Japanese or Mandarin) speaker dyads completed an unscripted, computer game-based task designed to create intelligibility challenges involving vowel tensity contrasts absent in Japanese and Mandarin. Acoustic analyses (F1, F2, duration) tracked vowel production changes throughout the interaction. Results reveal mutual phonetic adaptation, with both native and nonnative interlocutors enhancing vowel contrasts by shifting their L1-based cue-weighting strategies (spectral versus temporal) to better align with their partner’s patterns. However, in a post-conversation word-reading task, speakers reverted to their original productions, suggesting that adaptations were driven by immediate communicative needs. Additional analyses examine how dyads resolved miscommunications tied to each target word, further establishing the intelligibility-driven nature of these adaptations. Findings underscore a dynamic, collaborative, and goal-oriented process of phonetic adaptation in cross-linguistic interaction, where both native and nonnative speakers strategically modify their speech to improve shared intelligibility.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".