GatedxLSTM: A Multimodal Affective Computing Approach for Emotion Recognition in Conversations

• URL: http://arxiv.org/abs/2503.20919v1
• Date: Wed, 26 Mar 2025 18:46:18 GMT
• Title: GatedxLSTM: A Multimodal Affective Computing Approach for Emotion Recognition in Conversations
• Authors: Yupei Li, Qiyang Sun, Sunil Munthumoduku Krishna Murthy, Emran Alturki, Björn W. Schuller,
• Abstract summary: GatedxLSTM is a novel multimodal Emotion Recognition in Conversation (ERC) model.<n>It considers voice and transcripts of both the speaker and their conversational partner to identify the most influential sentences driving emotional shifts.<n>It achieves state-of-the-art (SOTA) performance among open-source methods in four-class emotion classification.
• Score: 35.63053777817013
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Affective Computing (AC) is essential for advancing Artificial General Intelligence (AGI), with emotion recognition serving as a key component. However, human emotions are inherently dynamic, influenced not only by an individual's expressions but also by interactions with others, and single-modality approaches often fail to capture their full dynamics. Multimodal Emotion Recognition (MER) leverages multiple signals but traditionally relies on utterance-level analysis, overlooking the dynamic nature of emotions in conversations. Emotion Recognition in Conversation (ERC) addresses this limitation, yet existing methods struggle to align multimodal features and explain why emotions evolve within dialogues. To bridge this gap, we propose GatedxLSTM, a novel speech-text multimodal ERC model that explicitly considers voice and transcripts of both the speaker and their conversational partner(s) to identify the most influential sentences driving emotional shifts. By integrating Contrastive Language-Audio Pretraining (CLAP) for improved cross-modal alignment and employing a gating mechanism to emphasise emotionally impactful utterances, GatedxLSTM enhances both interpretability and performance. Additionally, the Dialogical Emotion Decoder (DED) refines emotion predictions by modelling contextual dependencies. Experiments on the IEMOCAP dataset demonstrate that GatedxLSTM achieves state-of-the-art (SOTA) performance among open-source methods in four-class emotion classification. These results validate its effectiveness for ERC applications and provide an interpretability analysis from a psychological perspective.

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