Work

DualDistill: Self and cross-modal knowledge distillation for multimodal emotion recognition in conversation

Year: 2026

Type: article

Abstract: Emotion Recognition in Conversation (ERC) has become increasingly critical in diverse applications, including health care and virtual assistants. This paper proposes DualDistill, a multimodal ERC model. The proposed model employs a self-distillation strategy based on the Exponential Moving Average (EMA) to incorporate soft-label signals and enhance text representations, providing rich emotional cues. In addition, cross-modal Knowledge Distillation (KD) is applied to transfer contextual emotional cues from text to non-verbal modalities (audio and visual), alleviating modality imbalance and improving multimodal fusion. The DualDistill method achieves state-of-the-art performance on IEMOCAP and MELD benchmarks, demonstrating robustness and strong generalizability.

Source: ICT Express

DOI: 10.1016/j.icte.2026.04.004

Authors: DeogHwa Kim, DeogHwa Kim, Deok-Hwan Kim, Deok-Hwan Kim

Institution: Inha University

Language: English

Topic: Emotion and Mood Recognition

Subfield: Experimental and Cognitive Psychology

Field: Psychology

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $1,500

Funders: Inha University, National Research Foundation of Korea, Ministry of Science and ICT, South Korea