Papers with IEMOCAP datasets
Mitigating Inconsistencies in Multimodal Sentiment Analysis under Uncertain Missing Modalities (2022.emnlp-main)
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| Challenge: | Existing studies ignore the inconsistency phenomenon of missing modality in multimodal sentiment analysis . neglect of missing modalities may lead to incorrect semantic results . |
| Approach: | They propose an ensemble-based Missing Modality Reconstruction network to detect and recover missing modality features. |
| Outcome: | The proposed method is superior to existing methods on CMU-MOSI and IEMOCAP datasets. |
Emotion Recognition in Multi-Speaker Conversations through Speaker Identification, Knowledge Distillation, and Hierarchical Fusion (2026.findings-eacl)
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| Challenge: | Emotion recognition in multi-speaker conversations faces significant challenges due to speaker ambiguity and severe class imbalance. |
| Approach: | They propose a speaker identification module that leverages audio-visual synchronization to accurately identify the active speaker and hierarchical attention fusion with composite loss functions to handle class imbalance. |
| Outcome: | The proposed framework achieves 67.75% and 72.44% weighted F1 scores on MELD and IEMOCAP datasets, with notable improvements on minority emotion classes. |
Estimating the Uncertainty in Emotion Attributes using Deep Evidential Regression (2023.acl-long)
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| Challenge: | Existing methods to predict human emotions are inconsistent due to complexity of emotion and subjectivity of perception. |
| Approach: | They propose a Bayesian approach to estimate uncertainty in emotion attributes using a deep neural network model. |
| Outcome: | The proposed approach estimates uncertainty in emotion attributes along with aleatoric and epistemic uncertainties. |
EMO-RL: Emotion-Rule-Based Reinforcement Learning Enhanced Audio-Language Model for Generalized Speech Emotion Recognition (2025.findings-emnlp)
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| Challenge: | Recent advances in reinforcement learning (RL) have shown promise in improving LALMs’ reasoning abilities, but their performance in affective computing tasks remains suboptimal. |
| Approach: | They propose a framework incorporating reinforcement learning with two key innovations: Emotion Similarity-Weighted Reward (ESWR) and Explicit Structured Reasoning (ESR). |
| Outcome: | The proposed framework improves LALMs' reasoning abilities on MELD and IEMOCAP datasets and shows strong generalization. |