Work

Multi-HM: A Chinese Multimodal Dataset and Fusion Framework for Emotion Recognition in Human–Machine Dialogue Systems

Year: 2025

Type: article

Abstract: Sentiment analysis is pivotal in advancing human–computer interaction (HCI) systems as it enables emotionally intelligent responses. While existing models show potential for HCI applications, current conversational datasets exhibit critical limitations in real-world deployment, particularly in capturing domain-specific emotional dynamics and context-sensitive behavioral patterns—constraints that hinder semantic comprehension and adaptive capabilities in task-driven HCI scenarios. To address these gaps, we present Multi-HM, the first multimodal emotion recognition dataset explicitly designed for human–machine consultation systems. It contains 2000 professionally annotated dialogues across 10 major HCI domains. Our dataset employs a five-dimensional annotation framework that systematically integrates textual, vocal, and visual modalities while simulating authentic HCI workflows to encode pragmatic behavioral cues and mission-critical emotional trajectories. Experiments demonstrate that Multi-HM-trained models achieve state-of-the-art performance in recognizing task-oriented affective states. This resource establishes a crucial foundation for developing human-centric AI systems that dynamically adapt to users’ evolving emotional needs.

Source: Applied Sciences

DOI: 10.3390/app15084509

Authors: Yao Fu, Qiong Liu, Qing Song, Pengzhou Zhang, Gongdong Liao

Institutions: State Key Laboratory of Media Convergence and Communication, Communication University of China

Language: English

Topic: Emotion and Mood Recognition

Subfield: Experimental and Cognitive Psychology

Field: Psychology

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funder: National Key Research and Development Program of China