A linear model of acoustic-to-facial mapping: Model parameters, data set size, and generalization across speakers
Bibliographic record
Abstract
The relationship between acoustic and visual speech is important for understanding speech perception, but it also forms the basis behind a type of facial animator, which can predict facial motion during speech given an acoustic input. This relationship was examined by revisiting a linear transformation model of audio-visual speech production. A mathematical model is constructed whereby the visual aspect of speech is reproduced from the acoustic signal via a linear transformation. Unlike previous studies in this area, this paper will address specific aspects of the model as related to the effects of window size for acoustic framing and the critical size of the training set. On average, facial motion is predicted with a correlation of 0.70 to the recorded motion, when the model is trained and then tested on the same subject. This is comparable to previous studies using either similar or different model approaches. Using a model trained on other subjects and then applying it to a new subject resulted in a prediction correlation of 0.65. Furthermore, acoustic windows of 100 ms and a data set of approximately 40 sentences are required for maximum predictability. The results are interpreted in terms of the underlying assumptions of the model.
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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 | 0.013 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".