Summary of Character-aware Audio-visual Subtitling in Context, by Jaesung Huh et al.
Character-aware audio-visual subtitling in context
by Jaesung Huh, Andrew Zisserman
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Sound (cs.SD); Audio and Speech Processing (eess.AS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents an improved framework for character-aware audio-visual subtitling in TV shows, integrating speech recognition, speaker diarisation, and character recognition using both audio and visual cues. The approach addresses what is said, when it’s said, and who is speaking, providing a more comprehensive and accurate character-aware subtitling. This includes improving audio-visual synchronisation to pick out the talking face and assign an identity to corresponding speech segments, as well as determining speakers of short segments using local voice embeddings and large language model reasoning on text transcription. The proposed approach demonstrates superior performance in speaker diarisation and character recognition accuracy compared to existing approaches on a dataset with 12 TV shows. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it easier for TVs to show who’s talking and what they’re saying when you watch your favourite shows! It does this by combining three important steps: recognizing what people are saying, figuring out who’s speaking, and using both sound and video cues. This helps get the subtitles right so you know exactly what’s happening in the show. The new way of doing things works better than current methods on a special dataset with 12 TV shows. |
Keywords
» Artificial intelligence » Large language model
