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Inf. Syst."],"published-print":{"date-parts":[[2026,2,28]]},"abstract":"<jats:p>Multi-modal Conversation Emotion Recognition (MCER) aims to recognize and track the speaker\u2019s emotional state using text, speech, and visual information. Compared with traditional single-utterance multi-modal emotion recognition or single-modal conversation emotion recognition, MCER is more challenging. It requires modeling complex emotional interactions and learning consistent and complementary semantics across multiple modalities. Although many deep learning-based approaches have been proposed for MCER, there is still a lack of systematic reviews summarizing existing modeling methods. Therefore, a timely and comprehensive overview of MCER\u2019s recent advances in deep learning is of great significance. In this survey, we provide a comprehensive overview of MCER modeling methods and roughly divide MCER methods into four categories, i.e., context-free modeling, sequential context modeling, speaker-differentiated modeling, and speaker-relationship modeling. Unlike conventional taxonomies based on modality combinations or task-stage decomposition, our framework focuses on how models structurally capture conversational dynamics, speaker roles, and emotional dependencies. In addition, we further discuss MCER\u2019s publicly available popular datasets, multi-modal feature extraction methods, application areas, existing challenges, and future development directions. We hope this review provides valuable insights into the current state of MCER research and inspires the development of more effective models.<\/jats:p>","DOI":"10.1145\/3786343","type":"journal-article","created":{"date-parts":[[2025,12,23]],"date-time":"2025-12-23T13:04:07Z","timestamp":1766495047000},"page":"1-48","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":23,"title":["A Comprehensive Survey on Multi-modal Conversational Emotion Recognition with Deep Learning"],"prefix":"10.1145","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3270-6238","authenticated-orcid":false,"given":"Yuntao","family":"Shou","sequence":"first","affiliation":[{"name":"College of Computer and Mathematics, Central South University of Forestry and Technology, Changsha, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9787-2002","authenticated-orcid":false,"given":"Tao","family":"Meng","sequence":"additional","affiliation":[{"name":"College of Computer and Mathematics, Central South University of Forestry and Technology, Changsha, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9047-7977","authenticated-orcid":false,"given":"Wei","family":"Ai","sequence":"additional","affiliation":[{"name":"College of Computer and Mathematics, Central South University of Forestry and Technology, Changsha, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-5527-5982","authenticated-orcid":false,"given":"Fangze","family":"Fu","sequence":"additional","affiliation":[{"name":"College of Computer and Mathematics, Central South University of Forestry and Technology, Changsha, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0177-8514","authenticated-orcid":false,"given":"Nan","family":"Yin","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Mohamed bin Zayed University of Artificial Intelligence, Masdar City, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5224-4048","authenticated-orcid":false,"given":"Keqin","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computer Science, State University of New York at New Paltz, New Paltz, New\u00a0York, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,1,28]]},"reference":[{"key":"e_1_3_1_2_2","first-page":"23716","article-title":"Flamingo: A visual language model for few-shot learning","volume":"35","author":"Alayrac Jean-Baptiste","year":"2022","unstructured":"Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katherine Millican, Malcolm Reynolds, et al. 2022. 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