Multimodal Emotion Recognition for Conversational Systems in Continuous Affective Space
Bibliographic record
Abstract
Recognizing human emotions in a continuous affective space is challenging due to their dynamic, multimodal, and context-dependent nature. While the Valence-Arousal-Dominance (VAD) model offers a more precise representation than discrete classification, most continuous emotion recognition (CER) re-search remains unimodal (speech or text), limiting robustness. Speech-text fusion has improved performance, but bimodal approaches still fail to capture the full emotional complexity, emphasizing the need for a fully multimodal solution. To address this gap, we propose a fully multimodal model for CER in conver-sations, extending CORECT, originally designed for multimodal discrete emotion recognition (DER), to the continuous domain. By leveraging relational and temporal dependencies across audio, visual, and textual modalities, CORECT achieves state-of-the-art performance, with an average Concordance Correlation Coefficient (CCC) of 0.774, reflecting an approximately 20% relative improvement over speech-text fusion models (CCC = 0.648).
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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 | 0.000 | 0.000 |
| 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".