Papers › MMER: Multimodal Multi-task Learning for Speech Emotion Recognition
MMER: Multimodal Multi-task Learning for Speech Emotion Recognition
Sreyan Ghosh, Utkarsh Tyagi, S Ramaneswaran, Harshvardhan Srivastava, Dinesh Manocha
In this paper, we propose MMER, a novel Multimodal Multi-task learning approach for Speech Emotion Recognition. MMER leverages a novel multimodal network based on early-fusion and cross-modal self-attention between text and acoustic modalities and solves three novel auxiliary tasks for learning emotion recognition from spoken utterances. In practice, MMER outperforms all our baselines and achieves state-of-the-art performance on the IEMOCAP benchmark. Additionally, we conduct extensive ablation studies and results analysis to prove the effectiveness of our proposed approach.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Multimodal Emotion Recognition | IEMOCAP-4 | MMER | Accuracy | 81.7 | #6 of 11 | Archive leaderboard | report |
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