MATLAB Toolbox for Audiovisual Speech Processing
Bibliographic record
Abstract
Audiovisual speech processing has reached a stage of maturity where there are now numerous computational procedures needed to measure and assess multimodal signals. However, as is often the case, the results of these procedures are better known than the procedures themselves. This paper presents a MATLAB toolbox consisting of an extensive collection of tools we have developed over the past 10 years. These tools are not intended to be the final answer for multimodal speech analysis; rather they are presented as an easy-to-use and welldocumented library whose scope is sufficiently broad to be useful to both experts and novices. The toolbox includes procedures for measuring, organizing, modeling, and validating multiple streams of time-varying data, including acoustics, two- and threedimensional motions of the speaker. In addition to physical and derived (from video) marker data, new functions have been implemented that incorporate optical flow techniques based on the OpenCV library. When complete the toolbox will allow us to track human body gestures during speech from video noninvasively and to quantify the correspondences between different performance modalities within and across speakers. Index Terms: audiovisual speech, multimodal speech, face motion, optical flow, system identification, Matlab toolbox.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.129 | 0.064 |
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".