学习为嵌入式人工智能代理人生成情境敏感的后台微笑,在心理健康对话中有应用
Maneesh Bilalpur1, Mert Inan2, Dorsa Zeinali2
1University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
CEUR workshop proceedings
|December 2, 2024
概括
研究人员开发了一种生成模型,使体内代理产生后台微笑,增强类似人类的互动. 这项创新旨在通过使代理人在对话中更易于理解和更有效地改善治疗支持.
科学领域:
- 人与计算机的交互
- 人工智能的人工智能
- 心理学 心理学 心理学
背景情况:
- 由于心理健康资源的严重短缺,需要创新的解决方案来提供可访问的治疗支持.
- 嵌入式代理提供了一个有希望的,成本效益的补充传统的护理.
- 模拟非语言行为,如后台通道,对于治疗环境中的关系至关重要,但尚未得到充分探索.
研究的目的:
- 通过模拟后台微笑来提高体内代理人建立关系的能力.
- 在人类对话中调查背道式微笑持续时间和强度的预测因素.
- 开发和评估一种生成模型,用于在体内代理中产生现实的后台微笑.
主要方法:
- 在关于敏感话题的面对面对话中,注释后台道微笑.
- 分析了语音表情,语言和人口统计数据作为微笑强度的预测指标.
- 开发了一种基于注意力的生成模型,将听众信息纳入笑容的生产.
主要成果:
- 扬声器和听众的行为,包括语音线索和人口统计数据,显著预测后台微笑的强度.
- 听众信息提高了生成模型的性能,而不是以演讲者为中心的方法.
- 基于预测因素的条件发电显著提高了发电质量的经验测量.
结论:
- 后台微笑生产可以有效地被模拟为体内代理人的生成问题.
- 那些表现出背道式微笑的化身代理人被认为更像人.
- 具有模拟后台微笑的代理人为非个人对话提供了一个有吸引力的替代方案,增强了用户体验.
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