个性化情感感知对话代理？探索用户特质驱动的对话策略以增强交互
Personalizing Emotion-aware Conversational Agents? Exploring User Traits-driven Conversational Strategies for Enhanced Interaction
AI总结：
本研究通过实证探究用户性别、人格和文化背景如何影响其与多情绪多声音情感感知对话代理的交互策略，发现个性化定制能提升用户满意度与交互质量。
摘要：
对话代理（CAs）日益融入日常生活，但其高效应对用户情绪的能力仍在演进中。本研究探讨具有不同特质——性别、人格和文化背景——的用户在特定情绪场景中如何调整其与情感感知对话代理的交互策略。通过一个情感感知对话代理原型，该原型以男声和女声表达五种不同情绪（中性、快乐、悲伤、愤怒和恐惧），我们借助实证研究考察交互动态如何在不同声音和情绪情境下发生变化。研究结果揭示了基于个体特质的用户参与度和对话策略存在显著差异，强调了个性化、情感敏感交互的价值。通过分析定性和定量数据，我们证明根据用户特征定制对话代理能够提升用户满意度和交互质量。本工作凸显了持续研究的迫切需要，以设计不仅能识别而且能自适应地响应情感需求的对话代理，最终更有效地支持多样化的用户群体。
英文摘要：
Conversational agents (CAs) are increasingly embedded in daily life, yet their ability to navigate user emotions efficiently is still evolving. This study investigates how users with varying traits -- gender, personality, and cultural background -- adapt their interaction strategies with emotion-aware CAs in specific emotional scenarios. Using an emotion-aware CA prototype expressing five distinct emotions (neutral, happy, sad, angry, and fear) through male and female voices, we examine how interaction dynamics shift across different voices and emotional contexts through empirical studies. Our findings reveal distinct variations in user engagement and conversational strategies based on individual traits, emphasizing the value of personalized, emotion-sensitive interactions. By analyzing both qualitative and quantitative data, we demonstrate that tailoring CAs to user characteristics can enhance user satisfaction and interaction quality. This work underscores the critical need for ongoing research to design CAs that not only recognize but also adaptively respond to emotional needs, ultimately supporting a diverse user groups more effectively.
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。
