TMNet: Transformer-fused multimodal framework for emotion recognition via EEG and speech
Bibliographic record
Abstract
In the evolving field of emotion recognition, which intersects psychology, human–computer interaction, and social robotics, there is a growing demand for more advanced and accurate frameworks. The traditional reliance on single-modal approaches has given way to a focus on multimodal emotion recognition, which offers enhanced performance by integrating multiple data sources. This paper introduces TMNet, an innovative multimodal emotion recognition framework that leverages both speech and Electroencephalography (EEG) signals to deliver superior accuracy. This framework utilizes cutting-edge technology, employing a Transformer model to effectively fuse the CNN-BiLSTM and BiGRU architectures, creating a unified multimodal representation for enhanced emotion recognition performance. By utilizing a diverse set of datasets RAVDESS, SAVEE, TESS, and CREMA-D for speech, along with EEG signals captured via the Muse headband. The multimodal model achieves impressive accuracies of 98.89% for speech and EEG signal processing.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".