Different facial cues for different speech styles in Mandarin tone articulation
Bibliographic record
Abstract
Research has shown that facial articulatory cues aid speech perception. However, how such cues are employed in different speech styles remains unclear. This study examined facial articulatory features of Mandarin tones in clear versus conversational speech styles produced by 20 native Mandarin speakers. Using computer-vision and image-processing techniques, keypoints representing each speaker's head, eyebrow and lips were identified on video, and their movement trajectories during tone productions were tracked. Thirty-three features based on distance, time and kinematics (e.g., velocity) were subsequently computed to characterize the movements. Random forest and t-test analyses were then conducted to identify the significant features between the two speech styles. Results reveal that, across tones, clear relative to conversational style involves greater movement distance and velocity, reaching articulatory targets faster. Individual tone analyses further indicate that the faster target approximation in clear speech occurs for contour tones (2-4) but not the flat tone 1. The increase in distance and velocity in clear speech is reflected on more features for the most dynamic tone 3 than for the other tones. These results suggest that clear-speech modifications for tones can be exhibited through facial movements, involving hyper-articulation across tones and tone-specific adjustments aligned with individual tone trajectories.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".