解读话语背后的情绪：将基于语调的情感上下文整合到社交响应的VR代理中
Reading the Mood Behind Words: Integrating Prosody-Derived Emotional Context into Socially Responsive VR Agents
AI总结：
本文提出一种整合语调情感上下文的VR交互流程，通过实时语音情感识别模型提升对话代理的自然性和用户亲和力。
摘要：
在与具身对话代理的VR交互中，用户的情绪意图通常更多地通过如何表达而非说什么来传达。然而，大多数VR代理流程依赖于语音到文本处理，丢弃了语调线索，并且即使语义正确，也常产生情感不一致的回应。我们提出了一种情感上下文意识的VR交互流程，将语音情感视为基于大语言模型的对话代理中的显式对话上下文。一个实时的语音情感识别模型从语调中推断出用户的情感状态，所得到的情感标签被注入到代理的对话上下文中，以塑造回应的语气和风格。一项针对30名受试者的内组VR研究结果表明，在对话质量、自然性、参与度、亲和力和人性化方面有显著改进，93.3%的参与者更喜欢具有情感意识的代理。
英文摘要：
In VR interactions with embodied conversational agents, users' emotional intent is often conveyed more by how something is said than by what is said. However, most VR agent pipelines rely on speech-to-text processing, discarding prosodic cues and often producing emotionally incongruent responses despite correct semantics. We propose an emotion-context-aware VR interaction pipeline that treats vocal emotion as explicit dialogue context in an LLM-based conversational agent. A real-time speech emotion recognition model infers users' emotional states from prosody, and the resulting emotion labels are injected into the agent's dialogue context to shape response tone and style. Results from a within-subjects VR study (N=30) show significant improvements in dialogue quality, naturalness, engagement, rapport, and human-likeness, with 93.3% of participants preferring the emotion-aware agent.
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。
