利用人工智能生成的情感自我声音,引导人们实现理想自我

Cathy Mengying Fang, Phoebe Chua, Samantha Chan, Joanne Leong, Andria Bao, Pattie Maes
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摘要

情绪受过去经历的影响,在很大程度上影响着决策和目标追求。传统的个人发展认知行为技术依赖于心理想象来憧憬理想中的自我,但对于那些在可视化方面有困难的人来说可能效果不佳。本文介绍的情感自言自语(ESV)是一种新型系统,它结合了情感表达语言模型和语音克隆技术,能以用户自己的声音提供定制的回应。在一项有 60 名参与者参加的研究中,我们调查了 ESV 在引导个人实现理想自我方面的潜力。在所有三种条件下(ESV、纯文本和心理想象),我们观察到复原力、自信心、积极性和目标承诺都有所提高,但 ESV 条件被认为具有独特的吸引力和个性化。我们讨论了将生成的自我语音系统设计为针对不同场景的个性化行为干预的意义。
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Leveraging AI-Generated Emotional Self-Voice to Nudge People towards their Ideal Selves
Emotions, shaped by past experiences, significantly influence decision-making and goal pursuit. Traditional cognitive-behavioral techniques for personal development rely on mental imagery to envision ideal selves, but may be less effective for individuals who struggle with visualization. This paper introduces Emotional Self-Voice (ESV), a novel system combining emotionally expressive language models and voice cloning technologies to render customized responses in the user's own voice. We investigate the potential of ESV to nudge individuals towards their ideal selves in a study with 60 participants. Across all three conditions (ESV, text-only, and mental imagination), we observed an increase in resilience, confidence, motivation, and goal commitment, but the ESV condition was perceived as uniquely engaging and personalized. We discuss the implications of designing generated self-voice systems as a personalized behavioral intervention for different scenarios.
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