This report introduces our novel method named STHG for the Audio-Visual Diarization task of the Ego4D Challenge 2023. Our key innovation is that we model all the speakers in a video using a single, unified heterogeneous graph learning framework. Unlike previous approaches that require a separate component solely for the camera wearer, STHG can jointly detect the speech activities of all people including the camera wearer. Our final method obtains 61.1% DER on the test set of Ego4D, which significantly outperforms all the baselines as well as last year's winner. Our submission achieved 1st place in the Ego4D Challenge 2023. We additionally demonstrate that applying the off-the-shelf speech recognition system to the diarized speech segments by STHG produces a competitive performance on the Speech Transcription task of this challenge.

翻译：本报告介绍了我们为Ego4D 2023挑战赛的视听说话人日志任务提出的新颖方法STHG。我们的核心创新在于，采用单一统一的异构图学习框架对视频中所有说话人进行建模。与先前需要单独组件处理相机佩戴者的方法不同，STHG能够联合检测包括相机佩戴者在内的所有人的语音活动。我们的最终方法在Ego4D测试集上获得了61.1%的说话人日志错误率，显著优于所有基线模型以及去年冠军。本方案在Ego4D 2023挑战赛中荣获第一名。此外，我们证明，将现成的语音识别系统应用于STHG生成的说话人分段语音，可在该挑战的语音转录任务中取得具有竞争力的表现。