手语对话中的情感识别
Emotion Recognition in Sign Language Conversation
AI总结：
针对手语情感识别缺乏对话上下文的问题，提出eJSL Dialog数据集，并系统评估了从孤立视觉网络到多模态对话架构的模型，发现通用多模态对话情感识别模型在手语领域存在领域差距，需要开发面向手语的上下文感知视觉提取器并扩大对话数据集规模。
摘要：
对话中的情感识别是情感计算的核心组成部分，而当前手语情感数据集资源主要关注孤立句子，缺乏对话上下文。仅在这些孤立话语上训练的模型在现实场景中表现下降，因为它们无法利用历史对话流。为了解决这一结构性限制，我们将ERC任务引入手语视频分析，并提出了eJSL Dialog数据集。该数据集使用STUDIES语料库的脚本构建，包含1,920个视频样本，组织成480个独特的对话。我们使用从孤立视觉网络到多模态对话架构的模型对该数据集进行了系统基准测试。结果揭示了将通用多模态对话情感识别模型应用于手语时存在的领域差距。这些发现表明，明确需要针对手语的上下文感知视觉提取器，并指出扩大对话数据集规模以支持大规模预训练是未来研究的必要下一步。
英文摘要：
Emotion Recognition in Conversation is a core component of affective computing, while current sign language emotion datasets primarily focus on isolated sentences and lack conversational context. Models trained exclusively on these isolated utterances demonstrate degraded performance in real world scenarios because they cannot utilize historical dialogue flow. To address this structural limitation, we introduce the ERC task to sign language video analysis and propose the eJSL Dialog dataset. Constructed using the scripts from the STUDIES corpus, the dataset contains 1,920 video samples organized into 480 unique dialogues. We conduct systematic benchmarking on this dataset using models ranging from isolated visual networks to multimodal conversational architectures. The results suggest the feasibility of extending conversational ERC frameworks to sign-language dialogue under the current benchmark setting, while also revealing limitations in existing visual representations for capturing sign-specific affective cues, motivating future work on sign-specific visual modeling and larger sign-language conversational training resources.
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