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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- PossiblePossibly related (embedding) · 51%janaador0827-commits/simulacra-forge →
- PossiblePossibly related (embedding) · 48%VioletVision-3B →
- LinkedLinked via arxiv author · 85%Yuxuan Li →
“Reasoning LLM Improves Speaker Recognition in Long-form TV Dramas”
- LinkedLinked via arxiv author · 85%Lingxi Xie →
“Reasoning LLM Improves Speaker Recognition in Long-form TV Dramas”
- LinkedLinked via arxiv author · 85%Xinyue Huo →
“Reasoning LLM Improves Speaker Recognition in Long-form TV Dramas”
- LinkedLinked via arxiv author · 85%Jihao Qiu →
“Reasoning LLM Improves Speaker Recognition in Long-form TV Dramas”
- LinkedLinked via arxiv author · 85%Jiacheng Shao →
“Reasoning LLM Improves Speaker Recognition in Long-form TV Dramas”
- LinkedLinked via arxiv author · 85%Pengfei Chen →
“Reasoning LLM Improves Speaker Recognition in Long-form TV Dramas”