A Multimodal Deep Learning Approach to Emotion Detection and Identification
Bibliographic record
Abstract
Automated emotion recognition and identification and its subsequent challenges have a long history. More recently, intense scientific research on computer based evaluation of human emotions has arrived at a crossroad. Reputable scientists in the cognitive science domain consider that the system built on Ekman's seven basic emotions is vitiated by generalizations obtained on a reduced number of test cases. In contrast, computer scientists consider that the progress made so far in the theory and application of Neural Networks allows computers to increase the accuracy of emotion detection and identification. A Multimodal Convolutional Neural Network (MMCNN) for emotion detection and identification in near real-time, will be introduced in this paper. The MMCNN detects, identifies and tracks users' emotions, by reasoning on facial micro-expressions, on body motions and on speech. A CNN classifies the emotion into one of the 7 universal classes accepted so far. The deciding classifier then takes the scores generated from both the micro-expression detector and speech synthesizer to predict the emotion. The emotion class is validated using the Berkeley Expressivity Questionnaire. Results on testing the accuracy of the algorithm are given at the end of this paper.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.004 | 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".