Tone slips in Cantonese: Evidence for early phonological encoding
Bibliographic record
Abstract
This article examines speech errors in Cantonese with the aim of fleshing out a larger speech production architecture for encoding phonological tone. A corpus was created by extracting 2462 speech errors, including 668 tone errors, from audio recordings of natural conversations. The structure of these errors was then investigated in order to distinguish two contemporary approaches to tone in speech production. In the tonal frames account, tone is encoded like metrical stress, represented in abstract structural frames for a word. Because tone cannot be mis-selected in tonal frames, tone errors are expected to be rare and non-contextual, as observed with stress. An alternative is that tone is actively selected in phonological encoding like phonological segments. This approach predicts that tone errors will be relatively common and exhibit the contextual patterns observed with segments, like perseveration and anticipation. In our corpus, tone errors are the second most common type of error, and the majority of errors exhibit contextual patterns that parallel segmental errors. Building on prior research, a two-stage model of phonological tone encoding is proposed, following the patterns seen in tone errors: Tone is phonologically selected concurrently with segments, but then sequentially assigned after segments to a syllable.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".