Frequency-aware multimodal fusion framework for emotion recognition in conversation

ReH-FUSE: Reliability-Aware Hierarchical Fusion of Experts for Multimodal Emotion Recognition in Conversation

ReH-FUSE is proposed, a reliability-aware framework with dialogue-aware text, audio, and cross-modal experts, whose decision-level router first models the relative preference between text and audio and then balances the resulting unimodal mixture against the cross-modal expert.

Embracing Unimodal Aleatoric Uncertainty for Multimodal Emotion Recognition in Conversations

In conversational emotion recognition, every utterance is assigned an affective label using the textual, acoustic, and visual streams that accompany speech; however, each single-modality representation is typically contaminated by noise and ambiguous semantics, which harms cross-modal interaction and weakens fusion. We...

Differential interaction graph attention network for multimodal emotion recognition in conversations

Experiments on the IEMOCAP and MELD datasets show that Diff-IGAT significantly outperforms baseline methods, demonstrating its effectiveness in multi-party emotion recognition.

Differential Attention Unlocks Complementary EEG and Speech Fusion for Emotion Recognition

EmoSpeechBrain is introduced, a multimodal framework built on the insight that noise suppression is a precondition for effective fusion, and shows that once EEG noise is suppressed, fusion delivers gains that naive combination cannot.

EAM-DG: emotion-aware memory network with dual-gating mechanism for dialogue emotion recognition

Register-augmented attention and self-calibrated fusion for robust multimodal sentiment analysis

Results indicate that combining register-based representation stabilization with quality-aware fusion provides an effective solution for robust multimodal sentiment analysis, and RegCal-Net achieves competitive performance across multiple evaluation metrics, particularly improving fine-grained sentiment classification...