Speech adaptation in conversation: Effects of segment cue-weighting strategy for non-native speakers
Bibliographic record
Abstract
Research has investigated how speakers make phonetic adaptations by adjusting their speech sounds to either converge to or diverge from their interlocutor’s. Less is known about the dynamic adaptive strategies non-native speakers use to improve intelligibility when interacting with native speakers, particularly how their strategies change over the course of spontaneous conversations. The current study examines phonetic adaptations in English words contrasting tense and lax vowels (e.g., sheep-ship) in unscripted conversations between non-native Japanese English and native English speakers during an engaging computer game task. Japanese speakers have previously been found to rely on temporal cues (vowel length) for tensity distinctions due to their L1 cue-weighting strategy rather than spectral cues (vowel quality) that are predominantly used in English. Acoustic analyses are conducted to examine changes in non-native vowel productions before, during and after the conversation task. We predict that, in an attempt to overcome miscommunication, non-native speakers may initially lengthen the tense vowels to distinguish them from their lax counterparts. As the conversation progresses, non-native speakers may adapt to a more native-like cue-weighting pattern by shifting to spectral distinctions. Results are discussed in terms of adaptations to cue-weighting strategies for intelligibility gains by interlocutors of different linguistic backgrounds.
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 | 0.015 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".