{"ID":2836858,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.20106","arxiv_id":"2511.20106","title":"EM2LDL: A Multilingual Speech Corpus for Mixed Emotion Recognition through Label Distribution Learning","abstract":"This study introduces EM2LDL, a novel multilingual speech corpus designed to advance mixed emotion recognition through label distribution learning. Addressing the limitations of predominantly monolingual and single-label emotion corpora \\textcolor{black}{that restrict linguistic diversity, are unable to model mixed emotions, and lack ecological validity}, EM2LDL comprises expressive utterances in English, Mandarin, and Cantonese, capturing the intra-utterance code-switching prevalent in multilingual regions like Hong Kong and Macao. The corpus integrates spontaneous emotional expressions from online platforms, annotated with fine-grained emotion distributions across 32 categories. Experimental baselines using self-supervised learning models demonstrate robust performance in speaker-independent gender-, age-, and personality-based evaluations, with HuBERT-large-EN achieving optimal results. By incorporating linguistic diversity and ecological validity, EM2LDL enables the exploration of complex emotional dynamics in multilingual settings. This work provides a versatile testbed for developing adaptive, empathetic systems for applications in affective computing, including mental health monitoring and cross-cultural communication. The dataset, annotations, and baseline codes are publicly available at https://github.com/xingfengli/EM2LDL.","short_abstract":"This study introduces EM2LDL, a novel multilingual speech corpus designed to advance mixed emotion recognition through label distribution learning. Addressing the limitations of predominantly monolingual and single-label emotion corpora \\textcolor{black}{that restrict linguistic diversity, are unable to model mixed emo...","url_abs":"https://arxiv.org/abs/2511.20106","url_pdf":"https://arxiv.org/pdf/2511.20106v1","authors":"[\"Xingfeng Li\",\"Xiaohan Shi\",\"Junjie Li\",\"Yongwei Li\",\"Masashi Unoki\",\"Tomoki Toda\",\"Masato Akagi\"]","published":"2025-11-25T09:26:15Z","proceeding":"cs.CL","tasks":"[\"cs.CL\"]","methods":"[\"LoRA\"]","has_code":false,"code_links":[{"ID":606635,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2836858,"paper_url":"https://arxiv.org/abs/2511.20106","paper_title":"EM2LDL: A Multilingual Speech Corpus for Mixed Emotion Recognition through Label Distribution Learning","repo_url":"https://github.com/xingfengli/EM2LDL","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}