Bimodal Emotional Recognition based on Long Term Recurrent Convolutional Network
Bibliographic record
Abstract
Determining a person’s emotional state remains a non-trivial task relating to the ambiguous definition of the emotion itself and the different tools used to identify emotional aspects. In this study, we propose an Emotional Recognition System by adopting a multimodal approach that combines the based speech emotion features and the based facial expressions features. Accordingly, the proposed recognition system contains three parts. The first component will be reserved to extract the facial expressions features using a deep learning-based network called Long-Term Recurrent Convolutional Network (LRCN). The second component will be set aside to extract speech emotion features using the Alex-Net deep learning-based network. Finally, we dedicated the last component to combine the information learned from the two previous modalities through the late fusion. In order to evaluate the performance of the proposed approach, we tested it with the Ryerson Audio Visual Database of Emotional Speech and Song (RAVDESS) human emotions. Obtained results show that the accuracy rate was improved by using the fusion strategy. Indeed, the accuracy rate increased from 78.82 (for facial modality) and 76.39 (for speech modality) to 85.76.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".