Reasoning LLM Improves Speaker Recognition in Long-form TV Dramas
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{DramaS
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- Linked via arxiv authorYuxuan Li →
Reasoning LLM Improves Speaker Recognition in Long-form TV Dramas
- Linked via arxiv authorLingxi Xie →
Reasoning LLM Improves Speaker Recognition in Long-form TV Dramas
- Linked via arxiv authorXinyue Huo →
Reasoning LLM Improves Speaker Recognition in Long-form TV Dramas
- Linked via arxiv authorJihao Qiu →
Reasoning LLM Improves Speaker Recognition in Long-form TV Dramas
- Linked via arxiv authorJiacheng Shao →
Reasoning LLM Improves Speaker Recognition in Long-form TV Dramas
- Linked via arxiv authorPengfei Chen →
Reasoning LLM Improves Speaker Recognition in Long-form TV Dramas
- Linked via arxiv authorJiannan Ge →
Reasoning LLM Improves Speaker Recognition in Long-form TV Dramas
- Linked via arxiv authorKaiwen Duan →
Reasoning LLM Improves Speaker Recognition in Long-form TV Dramas
- Linked via arxiv authorQi Tian →
Reasoning LLM Improves Speaker Recognition in Long-form TV Dramas