Acoustic speech to lip feature mapping for multimedia applications
Bibliographic record
Abstract
This paper presents a quantitative analysis of the relationship between acoustic speech and corresponding lip features. The lip features, such as the lip width, inner lip height and outer lip height, are acquired by an extraction algorithm combining both color and edge information within a Markov random field (MRF) framework. Meanwhile, LSP (linear spectrum pairs) coefficients are used to parameterize the acoustic speech. LSP coefficients and the lip features are then used to train mapping models. The resulting models are used to estimate lip features from acoustic speech. From the results, we can see that the measured lip features match fairly well with the estimated lip features. The correlation coefficients between measured and estimated lip features are as high as 0.90. The estimation technique of lip features from acoustic speech gives a way to integrate acoustic and visual speech, which is very useful for speech driven face animation, audio-video synchronization and foreign film dubbing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".