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Speech deepfakes are artificial voices generated by machine learning models. Previous literature has highlighted deepfakes as one of the biggest security threats arising from progress in artificial intelligence due to their potential for misuse. However, studies investigating human detection capabilities are limited. We presented genuine and deepfake audio to n = 529 individuals and asked them to identify the deepfakes. We ran our experiments in English and Mandarin to understand if language affects detection performance and decision-making rationale. We found that detection capability is unreliable. Listeners only correctly spotted the deepfakes 73% of the time, and there was no difference in detectability between the two languages. Increasing listener awareness by providing examples of speech deepfakes only improves results slightly. As speech synthesis algorithms improve and become more realistic, we can expect the detection task to become harder. The difficulty of detecting speech deepfakes confirms their potential for misuse and signals that defenses against this threat are needed.

语音深度伪造（speech deepfakes）是由机器学习模型生成的人工合成语音。现有研究已指出，鉴于其存在被滥用的潜在可能，语音深度伪造堪称人工智能技术进步所催生的最严峻安全威胁之一。然而，针对人类识别语音深度伪造能力的相关研究仍较为有限。我们向共计529名受试者展示了原始语音与语音深度伪造音频，并要求其甄别其中的伪造语音。我们分别以英语和普通话开展实验，以探究语言类型是否会对受试者的识别表现与决策依据产生影响。实验结果表明，人类的语音深度伪造识别能力并不可靠：受试者仅能以73%的准确率识别出伪造语音，且两种语言场景下的识别准确率并无显著差异。通过提供语音深度伪造示例以提升受试者的认知水平，仅能小幅改善其识别效果。随着语音合成算法不断迭代优化、生成的语音愈发逼真，语音深度伪造的识别任务难度也将随之提升。语音深度伪造的识别难度印证了其被滥用的潜在风险，同时也警示亟需针对此类安全威胁构建防御体系。