A simplified audiovisual fusion model with application to large-vocabulary recognition of French Canadian speech
Bibliographic record
Abstract
A new, simple and practical way of fusing audio and visual information to enhance audiovisual automatic speech recognition within the framework of an application of large-vocabulary speech recognition of French Canadian speech is presented, and the experimental methodology is described in detail. The visual information about mouth shape is extracted off-line using a cascade of weak classifiers and a Kalman filter, and is combined with the large-vocabulary speech recognition system of the Centre de Recherche Informatique de Montreal. The visual classification is performed by a pair-wise kernel-based linear discriminant analysis (KLDA) applied on a principal component analysis (PCA) subspace, followed by a binary combination and voting algorithm on 35 French phonetic classes. Three fusion approaches are compared: (1) standard low-level feature-based fusion, (2) decision-based fusion within the framework of the transferable belief model (an interpretation of the Dempster-Shafer evidential theory), and (3) a combination of (1) and (2). For decision-based fusion, the audio information is considered to be a precise Bayesian source, while the visual information is considered an imprecise evidential source. This treatment ensures that the visual information does not significantly degrade the audio information in situations where the audio performs well (e.g., a controlled noise-free environment). Results show significant improvement in the word error rate to a level comparable to that of more sophisticated systems. To the authors' knowledge, this work is the first to address large-vocabulary audiovisual recognition of French Canadian speech and decision-based audiovisual fusion within the transferable belief model.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".