Improved Audio-Visual Laughter Detection Via Multi-Scale Multi-Resolution Image Texture Features and Classifier Fusion
Bibliographic record
Abstract
Efforts are afoot to design better context-aware human-computer interaction techniques that have knowledge of both their surrounding and the affective state of the user. One of the most important nonverbal behavioural cues for affective human-machine interaction is laughter. Automatic detection of laughter is an interesting, yet challenging problem, which in recent years has gained increased attention from both the academic and industrial communities. The majority of existing laughter detection systems rely on either audio or video modalities. Humans, however, typically rely on audio-visual cues during conversation and/or interaction, thus it is expected that improved results can be achieved if both modalities are used. In this work, we propose a multimodal framework that analyzes audio and video channels separately, then fuses their decisions. Conventional speech spectral and prosodic features are used, whereas new multi -scale multiresolution binarized statistical image features are proposed due to their improved expressive power. Experiments with the publicly available MAHNOB Laughter database show that decision level fusion based on support vector machine classifiers leads to improved performance over single modality approaches, as well as over previously-proposed methods, all whilst requiring just a fraction of the computational power.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".