Leveraging AI-Generated Emotional Self-Voice to Nudge People towards their Ideal Selves

要旨

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, and 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.

著者
Cathy Mengying Fang
MIT Media Lab, Cambridge, Massachusetts, United States
Phoebe Chua
School of Computing, National University of Singapore, Singapore, Singapore
Samantha W. T.. Chan
Nanyang Technological University, Singapore, Singapore
Joanne Leong
MIT, Cambridge, Massachusetts, United States
Andria Bao
Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
Pattie Maes
MIT Media Lab, Cambridge, Massachusetts, United States
DOI

10.1145/3706598.3713359

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713359

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Agent Design

G301
7 件の発表
2025-05-01 01:20:00
2025-05-01 02:50:00
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