Large Language Models and commercial speech synthesis systems now enable highly realistic AI-generated voice scams (vishing), raising urgent concerns about deception at scale. Yet it remains unclear whether individuals can reliably distinguish AI-generated speech from human-recorded voices in realistic scam contexts and what perceptual strategies underlie their judgments. We conducted a controlled online study in which 22 participants evaluated 16 vishing-style audio clips (8 AI-generated, 8 human-recorded) and classified each as human or AI while reporting confidence. Participants performed poorly: mean accuracy was 37.5%, below chance in a binary classification task. At the stimulus level, misclassification was bidirectional: 75% of AI-generated clips were majority-labeled as human, while 62.5% of human-recorded clips were majority-labeled as AI. Signal Detection Theory analysis revealed near-zero discriminability (d' approx 0), indicating inability to reliably distinguish synthetic from human voices rather than simple response bias. Qualitative analysis of 315 coded excerpts revealed reliance on paralinguistic and emotional heuristics, including pauses, filler words, vocal variability, cadence, and emotional expressiveness. However, these surface-level cues traditionally associated with human authenticity were frequently replicated by AI-generated samples. Misclassifications were often accompanied by moderate to high confidence, suggesting perceptual miscalibration rather than uncertainty. Together, our findings demonstrate that authenticity judgments based on vocal heuristics are unreliable in contemporary vishing scenarios. We discuss implications for security interventions, user education, and AI-mediated deception mitigation.

翻译：大型语言模型和商业语音合成系统如今能够生成高度逼真的AI语音诈骗（语音钓鱼），这引发了关于大规模欺骗的紧迫担忧。然而，在真实的诈骗场景中，个体是否能够可靠地区分AI生成的语音与人类录制的语音，以及其判断依赖何种感知策略，仍不明确。我们开展了一项受控在线研究，22名参与者评估了16段语音钓鱼风格的音频片段（8段AI生成、8段人类录制），并对每段音频判断其来源是人类还是AI，同时报告置信度。参与者的表现较差：平均准确率为37.5%，在二分类任务中低于随机水平。在刺激层面，错误分类是双向的：75%的AI生成片段被多数人标记为人类，而62.5%的人类录制片段被多数人标记为AI。信号检测理论分析显示区分度接近零（d'约等于0），表明参与者无法可靠区分合成语音与人类语音，而非简单的反应偏向。对315个编码摘录的定性分析显示，参与者依赖副语言和情感启发式，包括停顿、填充词、语音变化、节奏和情感表现力。然而，这些传统上被视为人类真实性的表层线索，却常被AI生成样本所复制。错误分类通常伴随中等至高的置信度，这表明感知校准偏差而非不确定性。综合而言，我们的发现表明，在当代语音钓鱼场景中，基于语音启发式的真实性判断并不可靠。我们讨论了这些发现对安全干预、用户教育以及AI中介欺骗缓解的影响。