Title: VoiceMorph: How AI Voice Morphing Reveals the Boundaries of Auditory Self-Recognition Show Chinese title

Title: VoiceMorph：人工智能语音变形如何揭示听觉自我识别的边界

Abstract: This study investigated auditory self-recognition boundaries using AI voice morphing technology, examining when individuals cease recognizing their own voice. Through controlled morphing between participants' voices and demographically matched targets at 1% increments using a mixed-methods design, we measured self-identification ratings and response times among 21 participants aged 18-64. Results revealed a critical recognition threshold at 35.2% morphing (95% CI [31.4, 38.1]). Older participants tolerated significantly higher morphing levels before losing self-recognition ($\beta$ = 0.617, p = 0.048), suggesting age-related vulnerabilities. Greater acoustic embedding distances predicted slower decision-making ($r \approx 0.5-0.53, p < 0.05$), with the longest response times for cloned versions of participants' own voices. Qualitative analysis revealed prosodic-based recognition strategies, universal voice manipulation discomfort, and awareness of applications spanning assistive technology to security risks. These findings establish foundational evidence for individual differences in voice morphing detection, with implications for AI ethics and vulnerable population protection as voice synthesis becomes accessible. Show Chinese abstract

Abstract: 本研究利用人工智能语音变形技术探讨了听觉自我识别的边界，考察个体何时停止识别自己的声音。 通过使用混合方法设计，在参与者的声音和人口统计学匹配的目标之间以1%的增量进行受控变形，我们测量了21名年龄在18至64岁之间的参与者的自我认同评分和反应时间。 结果揭示了一个关键的识别阈值，为35.2%的变形（95% CI [31.4, 38.1]）。 年长的参与者在失去自我识别前能容忍显著更高的变形水平（$\beta$= 0.617，p = 0.048），这表明存在与年龄相关的脆弱性。 更大的声学嵌入距离预测了更慢的决策过程（$r \approx 0.5-0.53, p < 0.05$），其中对参与者自己声音的克隆版本反应时间最长。 定性分析揭示了基于语调的识别策略、普遍的语音操纵不适感以及对从辅助技术到安全风险的应用的认识。 这些发现为语音变形检测中的个体差异提供了基础证据，对人工智能伦理和易受影响人群的保护具有意义，随着语音合成的普及，这一点尤为重要。