arXiv:2607.02504v1 Announce Type: new 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 sp
The development of more sophisticated large language models and the increasing demand for robust video understanding capabilities are converging to advance AI applications in media analysis.
Improved speaker recognition in long-form content is critical for automating content analysis, enhancing accessibility, and enabling advanced AI agentic systems to process complex real-world social interactions.
AI systems can now more accurately identify and attribute speech to specific characters in challenging, real-world, long-form video, moving beyond controlled datasets to complex narratives.
- · AI developers
- · Media entertainment industry
- · Content analysis companies
- · Accessibility technology providers
- · Manual transcription services
- · Legacy speech recognition systems
Automated character indexing and narrative understanding improve significantly for film and television archives.
This capability could extend to real-time analysis of live events or complex, multi-speaker virtual environments, enabling more nuanced AI-driven interactions.
Enhanced understanding of social dynamics within media could inform the development of more human-like AI agents, capable of contextual social reasoning.
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Read at arXiv cs.CL