Recently, sign language researchers have turned to sign language interpreted TV broadcasts, comprising (i) a video of continuous signing and (ii) subtitles corresponding to the audio content, as a readily available and large-scale source of training data. One key challenge in the usability of such data is the lack of sign annotations. Previous work exploiting such weakly-aligned data only found sparse correspondences between keywords in the subtitle and individual signs. In this work, we propose a simple, scalable framework to vastly increase the density of automatic annotations. Our contributions are the following: (1) we significantly improve previous annotation methods by making use of synonyms and subtitle-signing alignment; (2) we show the value of pseudo-labelling from a sign recognition model as a way of sign spotting; (3) we propose a novel approach for increasing our annotations of known and unknown classes based on in-domain exemplars; (4) on the BOBSL BSL sign language corpus, we increase the number of confident automatic annotations from 670K to 5M. We make these annotations publicly available to support the sign language research community.

本文提出了一种基于同义词和字幕-手语对齐的简单可扩展框架，用于自动标注手语解释电视广播中的连续手语视频，并利用标注的手语识别模型进行了假标注，从而增加已知和未知类别的标注密度。该方法可将BOBSL BSL手语语料库的信心自动注释数量从670K提高到5M，并提供这些注释以支持手语研究社区。

大词汇手语视频的自动密集标注