Papers › Speech Emotion Recognition with Multi-Task Learning
Speech Emotion Recognition with Multi-Task Learning
Cai, Xingyu Yuan, Jiahong Zheng, Renjie Huang, Liang Church, Kenneth
Speech emotion recognition (SER) classifies speech into emotion categories such as: Happy, Angry, Sad and Neutral. Recently , deep learning has been applied to the SER task. This paper proposes a multi-task learning (MTL) framework to simultaneously perform speech-to-text recognition and emotion classification, with an end-to-end deep neural model based on wav2vec-2.0. Experiments on the IEMOCAP benchmark show that the proposed method achieves the state-of-the-art performance on the SER task. In addition, an ablation study establishes the effectiveness of the proposed MTL framework.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Speech Emotion Recognition | IEMOCAP | SER with MTL | F1 | - | #1 of 8 | Archive leaderboard | report |
| Speech Emotion Recognition | IEMOCAP | SER with MTL | UA CV | 0.7815 | #1 of 8 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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