Computer-vision analysis shows different facial movements for the production of different Mandarin tones
Bibliographic record
Abstract
We aim to identify visual cues resulting from facial movements made during Mandarin tone production and examine how they are associated with each of the four tones. We use signal processing and computer vision techniques to analyze audio-video recordings of 21 native Mandarin speakers uttering the vowel /ɜ/ with each tone. Four facial interest points were automatically detected and tracked in the video frames: medial point of left-eyebrow, nose tip (proxy for head movement), and midpoints of the upper and lower lips. Spatiotemporal features were extracted from the positional profiles of each tracked point. These features included distance, velocity, and acceleration of local facial movements with respect to the resting face of each speaker. Analysis of variance and feature importance analysis based on random decision forest were performed to examine the significance of each feature for representing each tone and how well these features can individually and collectively characterize each tone. Preliminary results suggest alignments between articulatory movements and pitch trajectories, with downward or upward head and eyebrow movements following the dipping and rising tone trajectories, faster lip-closing toward the end of falling tone production, and minimal movements for the level tone.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".