The goal of this paper is speaker diarisation of videos collected 'in the wild'. We make three key contributions. First, we propose an automatic audio-visual diarisation method for YouTube videos. Our method consists of active speaker detection using audio-visual methods and speaker verification using self-enrolled speaker models. Second, we integrate our method into a semi-automatic dataset creation pipeline which significantly reduces the number of hours required to annotate videos with diarisation labels. Finally, we use this pipeline to create a large-scale diarisation dataset called VoxConverse, collected from 'in the wild' videos, which we will release publicly to the research community. Our dataset consists of overlapping speech, a large and diverse speaker pool, and challenging background conditions.
翻译：本文的目标是对“ 野生” 收集的视频进行语音分解。 我们做出三大贡献。 首先, 我们提出YouTube视频的自动视听分解方法。 我们的方法包括使用视听方法和自我放大的演讲模型对演讲者进行积极的检测。 其次, 我们将我们的方法整合到半自动数据元件创建管道中, 从而大大减少用分解标签对视频进行批注所需的小时数。 最后, 我们利用这条管道来创建大规模分解数据集, 名为“ 野生” 视频中收集的VoxConvers, 我们将向研究界公开发布这些数据。 我们的数据集由相互重叠的演讲、 大型和多样化的演讲者库以及具有挑战性的背景条件组成 。
相关内容
微信扫码咨询专知VIP会员