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• W4386826611 endingPage "121692" @default.
• W4386826611 startingPage "121692" @default.
• W4386826611 abstract "Emotion recognition has recently attracted extensive interest due to its significant applications to human-computer interaction. The expression of human emotion depends on various verbal and non-verbal languages like audio, visual, text, etc. Emotion recognition is thus well suited as a multimodal rather than single-modal learning problem. Owing to the powerful feature learning capability, extensive deep learning methods have been recently leveraged to capture high-level emotional feature representations for multimodal emotion recognition (MER). Therefore, this paper makes the first effort in comprehensively summarize recent advances in deep learning-based multimodal emotion recognition (DL-MER) involved in audio, visual, and text modalities. We focus on: (1) MER milestones are given to summarize the development tendency of MER, and conventional multimodal emotional datasets are provided; (2) The core principles of typical deep learning models and its recent advancements are overviewed; (3) A systematic survey and taxonomy is provided to cover the state-of-the-art methods related to two key steps in a MER system, including feature extraction and multimodal information fusion; (4) The research challenges and open issues in this field are discussed, and promising future directions are given." @default.
• W4386826611 created "2023-09-19" @default.
• W4386826611 creator A5015552513 @default.
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• W4386826611 creator A5064977471 @default.
• W4386826611 creator A5076492280 @default.
• W4386826611 creator A5086152036 @default.
• W4386826611 date "2024-03-01" @default.
• W4386826611 modified "2023-09-27" @default.
• W4386826611 title "Deep Learning-based Multimodal Emotion Recognition from Audio, Visual, and Text Modalities: A Systematic Review of Recent Advancements and Future Prospects" @default.
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