Model selection table for Experiment 3.

Modern artificial intelligence (AI) technology is capable of generating human sounding voices that could be used to deceive recipients in various contexts (e.g., deep fakes). Given the increasing accessibility of this technology and its potential societal implications, the present study conducted online experiments using original data to investigate the validity of AI-based voice similarity measures and their impact on trustworthiness and likability. Correlation analyses revealed that voiceprints – numerical representations of voices derived from a speaker verification system – can be used to approximate human (dis)similarity ratings. With regard to cognitive evaluations, we observed that voices similar to one’s own voice increased trustworthiness and likability, whereas average voices did not elicit such effects. These findings suggest a preference for self-similar voices and underscore the risks associated with the misuse of AI in generating persuasive artificial voices from brief voice samples.

当前人工智能（AI）技术可生成类人语音，能够在多种场景中对语音接收者实施欺骗（例如深度伪造（deep fakes））。随着该技术的可及性不断提升及其潜在的社会影响日益凸显，本研究采用原始数据开展线上实验，旨在探究基于AI的语音相似度度量方法的有效性，及其对可信度与好感度的影响。相关分析结果显示，声纹——即从说话人验证系统中提取的语音数值表征——可用于近似人类对语音相似度（或差异度）的主观评分。在认知评价维度中，我们观察到与自身语音相似的语音会提升受众对其的可信度评价与好感度，而平均语音则未产生此类效应。上述研究结果表明人类存在对自我相似语音的偏好倾向，同时也凸显了滥用AI技术、通过简短语音样本生成具有说服力的人工语音所潜藏的风险。