Have Cantonese Tones Merged in Spontaneous Speech?
Bibliographic record
Abstract
This is the first variationist sociolinguistic study of Cantonese tone-merger using conversational recordings. These data differ from experimental data exploring tone mergers: the speech is continuous and spontaneous, the tones appear in diverse contexts, and speakers are from both Toronto and Hong Kong. We investigated the status of three reportedly ongoing mergers: T2/T5忍 / 引, T3/T6 印 / 孕, and T4/T6 仁 / 孕. We measured three cues (i.e., mean pitch, pitch at 90% duration of the syllable, and pitch slope) in 12,000 + tokens from thirty-two speakers. Using normalized duration and speaker pitch, mixed-effects models showed that unmerged tones are statistically distinguishable in spontaneous speech, but that two of the three “ongoing-merger” pairs are fully merged, and the third is nearly merged. Analyses included segmental and suprasegmental (i.e., phrasal position, word position, adjacent tones) factors affecting pitch. We found no differences between heritage and homeland speaker samples.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".