Look, Listen and Recognise: Character-Aware Audio-Visual Subtitling

Bruno Korbar, Jaesung Huh, Andrew Zisserman
1/22/2024
cs.CVcs.SDeess.AS

No official code found. Best implementation: JaesungHuh/ca-subtitle (21 stars, Apache-2.0, last commit 2025-11-03). 1 more below.

Abstract

The goal of this paper is automatic character-aware subtitle generation. Given a video and a minimal amount of metadata, we propose an audio-visual method that generates a full transcript of the dialogue, with precise speech timestamps, and the character speaking identified. The key idea is to first use audio-visual cues to select a set of high-precision audio exemplars for each character, and then use these exemplars to classify all speech segments by speaker identity. Notably, the method does not require face detection or tracking. We evaluate the method over a variety of TV sitcoms, including Seinfeld, Fraiser and Scrubs. We envision this system being useful for the automatic generation of subtitles to improve the accessibility of the vast amount of videos available on modern streaming services. Project page : \url{https://www.robots.ox.ac.uk/~vgg/research/look-listen-recognise/}

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Code Implementations(2)

Apache-2.0

Implementation of "Look, Listen and Recognise:character-aware audio-visual subtitling"

212Shell, PythonJul 26, 202511 months agoApache-2.0
speaker-diarizationsubtitle-generation
Apache-2.0

Dataset page for Look, Listen and Recognise : character-aware audio-visual subtitling (ICASSP 2024)

70PythonMay 22, 202411 months agoApache-2.0
audio-visual-learningcharacter-aware-audiovisual-subtitlingcharacter-recognitionspeaker-diarizationspeech-recognition

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Cite this paper

@article{korbar2024look,
  title  = {Look, Listen and Recognise: Character-Aware Audio-Visual Subtitling},
  author = {Bruno Korbar and Jaesung Huh and Andrew Zisserman},
  year   = {2024},
  eprint = {2401.12039},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2401.12039v1}
}

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