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

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Dettagli Bibliografici
Pubblicato in:	Applied Sciences vol. 15, no. 8 (2025), p. 4509
Autore principale:	Fu, Yao
Altri autori:	Liu, Qiong, Song, Qing, Zhang Pengzhou, Liao Gongdong
Pubblicazione:	MDPI AG
Soggetti:	Affect (Psychology) Television programs Datasets Sentiment analysis Emotions Feedback Interactive computer systems Business metrics Benchmarks
Accesso online:	Citation/Abstract Full Text + Graphics Full Text - PDF
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Descrizione
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.
ISSN:	2076-3417
DOI:	10.3390/app15084509
Fonte:	Publicly Available Content Database