The contrast between clear and plain speaking style for Mandarin tones
Bibliographic record
Abstract
We examine the acoustic characteristics of clear and plain conversational productions of Mandarin tones. Twenty-one native Mandarin speakers were asked to produce a selection of Mandarin words in both plain and clear speaking styles. Several tokens were gathered for each of the four tones giving a total of 2045 productions. Six critical tonal cues were computed for each production: fundamental frequency (F0) mean, slope, and second derivative, duration, mean intensity, and a binary variable coding whether the production involved creaky voice. A linear mixed-effects regression model was used to explore how these cues changed with respect to the clear versus plain distinction for each tone, with speaking style as the fixed effect and speaker being a random effect. The strongest effects detected were that duration and mean intensity increased in clear speech across speakers and tones. Tones 2 and 3 increased in mean F0 and Tone 4 increased its slope. An additional finding was that, for contour tones, speakers accomplished the increase in duration by stretching out the tone contours in time while largely not changing the F0 range. These results are discussed in terms of signal-based (affecting all tones) and code-based (enhancing contrast between tones) change.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".