arXiv:2605.23328v1 Announce Type: new Abstract: Emotion Recognition in Conversation is a core component of affective computing, while current resources of 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 STU
The continuous advancements in AI and specifically in natural language processing (NLP) are naturally extending to more complex communication forms like sign language, driven by the emergence of robust conversational AI models.
This development is crucial for integrating sign language more fully into digital communication, enhancing accessibility, and enabling more nuanced human-computer interaction for deaf and hard-of-hearing communities.
Current AI models for sign language emotion recognition shift from isolated gesture analysis to understanding emotional context within dynamic conversations, significantly improving real-world applicability.
- · AI researchers in affective computing
- · Deaf and hard-of-hearing communities
- · Assistive technology developers
- · Digital communication platforms
- · Platforms with inaccessible communication interfaces
Improved accuracy in real-time sign language interpretation and emotional nuance understanding for digital interfaces.
Broader adoption of AI-powered assistive technologies and more inclusive digital environments for sign language users.
Potential for new forms of non-verbal human-computer or human-robot interaction that leverage emotional cues from visual language.
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