论文标题
用美国手语建模全球身体配置
Modeling Global Body Configurations in American Sign Language
论文作者
论文摘要
美国手语（ASL）是美国第四种最常用的语言，是聋人在美国和加拿大说英语地区最常用的语言。不幸的是，直到最近，ASL还是没有研究。这部分归因于它在1960年威廉·C·斯托科（William C. Stokoe）出版之前的延迟认可。有限的数据是ASL研究和计算建模的长期障碍。缺乏大规模数据集禁止许多现代的机器学习技术，例如神经机器翻译，无法应用于ASL。此外，捕获手语所需的方式（即视频）在自然设置中很复杂（因为必须处理背景噪声，运动模糊和维度的诅咒）。最后，与英语等语言相比，对ASL语言学进行了有限的研究。 我们使用概率图形模型（PGM）实现了Liddell和Johnson的运动含量（MH）模型的简化版本。我们在Asling上训练了模型，这是一个从三个流利的ASL签名者那里收集的数据集。我们对其他模型评估我们的PGM，以确定其建模ASL的能力。最后，我们解释了PGM的各个方面，并得出有关ASL语音学的结论。本文的主要贡献是
American Sign Language (ASL) is the fourth most commonly used language in the United States and is the language most commonly used by Deaf people in the United States and the English-speaking regions of Canada. Unfortunately, until recently, ASL received little research. This is due, in part, to its delayed recognition as a language until William C. Stokoe's publication in 1960. Limited data has been a long-standing obstacle to ASL research and computational modeling. The lack of large-scale datasets has prohibited many modern machine-learning techniques, such as Neural Machine Translation, from being applied to ASL. In addition, the modality required to capture sign language (i.e. video) is complex in natural settings (as one must deal with background noise, motion blur, and the curse of dimensionality). Finally, when compared with spoken languages, such as English, there has been limited research conducted into the linguistics of ASL. We realize a simplified version of Liddell and Johnson's Movement-Hold (MH) Model using a Probabilistic Graphical Model (PGM). We trained our model on ASLing, a dataset collected from three fluent ASL signers. We evaluate our PGM against other models to determine its ability to model ASL. Finally, we interpret various aspects of the PGM and draw conclusions about ASL phonetics. The main contributions of this paper are
