Phonetic adaptation in conversation: The case of Cantonese tone merging
Bibliographic record
Abstract
Phonetic adaptation occurs when one interlocutor adjusts their speech to converge to or diverge from that of their conversation partner to enhance intelligibility. While most research investigates segmental adaptations, our study focuses on suprasegmentals, specifically Cantonese tone merging. Some Cantonese speakers (“mergers”) are found to merge certain lexical tones (e.g., mid-level Tone3 and low-level Tone6), which may cause confusions when interacting with non-merger speakers. Previous research has shown that a merger may unmerge a level tone pair (Tone3/Tone6) when shadowing a non-merger. However, still unclear is whether such changes result from automatic acoustic mimicking or reflect goal-oriented adaptations for intelligibility benefits. This study uses an unscripted conversation task involving a merger and a non-merger playing a video game, where productions of merged tones may cause confusions, thus motivating goal-oriented adaptations. Initial acoustic analyses focus on average F0 and F0 taken at 10 points along the contour in target Tone3 and Tone6 productions by mergers. Differences in these values for Tone3 versus Tone6 provide evidence that a merger is unmerging the tone pair. Preliminary results show increasing unmerging trends as the task progresses, suggesting progressive alignment toward a non-merger’s productions for intelligibility gains.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".