ASL Sea Battle: Gamifying Sign Language Data Collection
ASL Sea Battle: Gamifying Sign Language Data Collection
复制标题
ASL 海战：游戏化手语数据收集
DOI：
--
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
W. Thies
中科院分区：
文献类型：
--
作者：
Danielle Bragg;Naomi K. Caselli;John W. Gallagher;Miriam Goldberg;Courtney J. Oka;W. Thies
为美国手语（ASL）等手语开发准确的机器学习模型有可能打破聋人手语的沟通障碍。然而，到目前为止，没有这样的模型已经足够强大的现实世界中使用。实现真实世界应用的主要障碍是缺乏适当的训练数据。现有的训练集有几个缺点：尺寸小，有限的签名者多样性，缺乏真实世界的设置，以及缺失或不准确的标签。在这项工作中，我们提出了ASL海战，一个手语游戏，旨在收集克服这些障碍的数据集，同时也为用户提供乐趣和教育。我们进行了一项用户研究，以探索游戏收集的数据质量以及玩游戏的用户体验。我们的研究结果表明，ASL Sea Battle可以可靠地收集和标记真实世界的手语视频，并以牺牲数据吞吐量为代价提供乐趣和教育。
The development of accurate machine learning models for sign languages like American Sign Language (ASL) has the potential to break down communication barriers for deaf signers. However, to date, no such models have been robust enough for real-world use. The primary barrier to enabling real-world applications is the lack of appropriate training data. Existing training sets suffer from several shortcomings: small size, limited signer diversity, lack of real-world settings, and missing or inaccurate labels. In this work, we present ASL Sea Battle, a sign language game designed to collect datasets that overcome these barriers, while also providing fun and education to users. We conduct a user study to explore the data quality that the game collects, and the user experience of playing the game. Our results suggest that ASL Sea Battle can reliably collect and label real-world sign language videos, and provides fun and education at the expense of data throughput.