语音变形：一个声音中的两个身份
Voice Morphing: Two Identities in One Voice
AI总结：
本研究提出语音身份变形（VIM）攻击，合成模仿两人声音特征的语音样本，在Librispeech数据集上对ECAPA-TDNN和x-vector系统实现超过80%的MMPMR成功率，揭示语音识别系统的安全漏洞。
摘要：
在生物识别系统中，每个生物识别样本或模板通常与单一身份相关联。然而，近期研究表明，有可能生成能够成功匹配多个身份的“变形”生物识别样本。变形攻击现已被视为对生物识别系统的潜在安全威胁。然而，大多数变形攻击的研究集中在图像域操作的生物识别模态上，如人脸、指纹和虹膜。在这项初步工作中，我们引入了语音身份变形（VIM）——一种基于语音的变形攻击，能够合成模仿一对个体声音特征的语音样本。我们的实验评估了两种流行的说话人识别系统ECAPA-TDNN和x-vector对VIM的脆弱性，在Librispeech数据集上，当误匹配率为1%时，成功率（MMPMR）超过80%。
英文摘要：
In a biometric system, each biometric sample or template is typically associated with a single identity. However, recent research has demonstrated the possibility of generating "morph" biometric samples that can successfully match more than a single identity. Morph attacks are now recognized as a potential security threat to biometric systems. However, most morph attacks have been studied on biometric modalities operating in the image domain, such as face, fingerprint, and iris. In this preliminary work, we introduce Voice Identity Morphing (VIM) - a voice-based morph attack that can synthesize speech samples that impersonate the voice characteristics of a pair of individuals. Our experiments evaluate the vulnerabilities of two popular speaker recognition systems, ECAPA-TDNN and x-vector, to VIM, with a success rate (MMPMR) of over 80% at a false match rate of 1% on the Librispeech dataset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
