手语者的情感识别
Emotion Recognition in Signers
AI总结：
研究手语者情感识别面临的挑战，通过跨语言设置，利用eJSL和BOBSL数据集，实验证明口语文本情感识别、时间片段选择及手部动作对增强手语情感识别的作用，建立更强基线。
摘要：
手语者情感识别面临语法和情感面部表情重叠以及模型训练数据稀缺这两个挑战。本文在跨语言环境中应对这些挑战，使用新的日语手语情感识别基准数据集eJSL和带字幕的大型英国手语数据集BOBSL。实验表明，口语中的文本情感识别可缓解手语数据稀缺问题，时间片段选择有显著影响，纳入手部动作可增强情感识别，最终建立了比口语语言模型更强的基线。
英文摘要：
Recognition of signers' emotions suffers from one theoretical challenge and one practical challenge, namely, the overlap between grammatical and affective facial expressions and the scarcity of data for model training. This paper addresses these two challenges in a cross-lingual setting using our eJSL dataset, a new benchmark dataset for emotion recognition in Japanese Sign Language signers, and BOBSL, a large British Sign Language dataset with subtitles. In eJSL, two signers expressed 78 distinct utterances with each of seven different emotional states, resulting in 1,092 video clips. We empirically demonstrate that 1) textual emotion recognition in spoken language mitigates data scarcity in sign language, 2) temporal segment selection has a significant impact, and 3) incorporating hand motion enhances emotion recognition in signers. Finally we establish a stronger baseline than spoken language LLMs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
