论文标题
WLASL-LEX：用于识别美国手语语音属性的数据集
WLASL-LEX: a Dataset for Recognising Phonological Properties in American Sign Language
论文作者
论文摘要
签名的语言处理（SLP）涉及签名语言的自动处理，这是聋哑人交流和听力障碍个人的主要手段。 SLP具有许多不同的任务，从标志识别到翻译和签名语音的生产，但到目前为止，NLP社区已经忽略了。在本文中，我们引起了对符号语言语音的建模的任务。我们利用现有资源来构建具有六种不同语音属性注释的美国手语标志的大规模数据集。然后，我们进行了一项广泛的经验研究，以研究是否可以优化数据驱动的端到端和基于特征的方法以自动识别这些特性。我们发现，尽管任务面临固有的挑战，但基于图的神经网络通过从原始视频中提取的骨架功能运行的基于图的神经网络能够在任务中成功地在不同的程度上取得成功。最重要的是，我们表明，即使在训练过程中未观察到的迹象，这种表现也是如此。
Signed Language Processing (SLP) concerns the automated processing of signed languages, the main means of communication of Deaf and hearing impaired individuals. SLP features many different tasks, ranging from sign recognition to translation and production of signed speech, but has been overlooked by the NLP community thus far. In this paper, we bring to attention the task of modelling the phonology of sign languages. We leverage existing resources to construct a large-scale dataset of American Sign Language signs annotated with six different phonological properties. We then conduct an extensive empirical study to investigate whether data-driven end-to-end and feature-based approaches can be optimised to automatically recognise these properties. We find that, despite the inherent challenges of the task, graph-based neural networks that operate over skeleton features extracted from raw videos are able to succeed at the task to a varying degree. Most importantly, we show that this performance pertains even on signs unobserved during training.
