Reasoning LLM Improves Speaker Recognition in Long-form TV Dramas
Research introduces DramaSR-532K, a 532K-line benchmark for speaker recognition in TV dramas, and proposes a reasoning LLM to improve accuracy using auditory, linguistic, and visual cues.
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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