{"ID":5676746,"CreatedAt":"2026-07-03T03:29:23.032456456Z","UpdatedAt":"2026-07-07T01:06:03.009715918Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.02504","arxiv_id":"2607.02504","title":"Reasoning LLM Improves Speaker Recognition in Long-form TV Dramas","abstract":"Long-form TV dramas present a formidable challenge for comprehensive video understanding, where deciphering complex storyline often relies on \\textbf{speaker recognition}, the task of accurately attributing each spoken utterance to its respective character. In this paper, we advance this field through two primary contributions. (1) We introduce \\textbf{DramaSR-532K}, a large-scale benchmark comprising 532K annotated dialogue lines across more than 900 unique characters, necessitating the integration of auditory, linguistic, and visual cues for speaker recognition. (2) We propose \\textbf{DramaSR-LRM}, a robust approach built upon a large reasoning model (LRM). DramaSR-LRM is designed to autonomously aggregate contextual evidence via multimodal tool-use, synthesizing diverse inputs to achieve high-fidelity attribution. Experimental results demonstrate that DramaSR-LRM significantly outperforms existing baselines, particularly on short utterances where acoustic biometrics are inherently unreliable. \\textit{All the data and code will be made publicly available at the project page: https://www.github.com/198808xc/DramaSR-LRM.}","short_abstract":"Long-form TV dramas present a formidable challenge for comprehensive video understanding, where deciphering complex storyline often relies on \\textbf{speaker recognition}, the task of accurately attributing each spoken utterance to its respective character. In this paper, we advance this field through two primary contr...","url_abs":"https://arxiv.org/abs/2607.02504","url_pdf":"https://arxiv.org/pdf/2607.02504v1","authors":"[\"Yuxuan Li\",\"Lingxi Xie\",\"Xinyue Huo\",\"Jihao Qiu\",\"Jiacheng Shao\",\"Pengfei Chen\",\"Jiannan Ge\",\"Kaiwen Duan\",\"Qi Tian\"]","published":"2026-07-02T17:58:52Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.AI\",\"cs.CV\"]","methods":"[\"Large Language Model\"]","has_code":false}