Multi-HM: A Chinese Multimodal Dataset and Fusion Framework for Emotion Recognition in Human–Machine Dialogue Systems
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| הוצא לאור ב: | Applied Sciences vol. 15, no. 8 (2025), p. 4509 |
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| מחבר ראשי: | |
| מחברים אחרים: | , , , |
| יצא לאור: |
MDPI AG
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| נושאים: | |
| גישה מקוונת: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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MARC
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| 003 | UK-CbPIL | ||
| 022 | |a 2076-3417 | ||
| 024 | 7 | |a 10.3390/app15084509 |2 doi | |
| 035 | |a 3194491923 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231338 |2 nlm | ||
| 100 | 1 | |a Fu, Yao | |
| 245 | 1 | |a Multi-HM: A Chinese Multimodal Dataset and Fusion Framework for Emotion Recognition in Human–Machine Dialogue Systems | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Affect (Psychology) | ||
| 653 | |a Television programs | ||
| 653 | |a Datasets | ||
| 653 | |a Sentiment analysis | ||
| 653 | |a Emotions | ||
| 653 | |a Feedback | ||
| 653 | |a Interactive computer systems | ||
| 653 | |a Business metrics | ||
| 653 | |a Benchmarks | ||
| 700 | 1 | |a Liu, Qiong | |
| 700 | 1 | |a Song, Qing | |
| 700 | 1 | |a Zhang Pengzhou | |
| 700 | 1 | |a Liao Gongdong | |
| 773 | 0 | |t Applied Sciences |g vol. 15, no. 8 (2025), p. 4509 | |
| 786 | 0 | |d ProQuest |t Publicly Available Content Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3194491923/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3194491923/fulltextwithgraphics/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3194491923/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |