Fake audio detection is an emerging active topic. A growing number of literatures have aimed to detect fake utterance, which are mostly generated by Text-to-speech (TTS) or voice conversion (VC). However, countermeasures against impersonation remain an underexplored area. Impersonation is a fake type that involves an imitator replicating specific traits and speech style of a target speaker. Unlike TTS and VC, which often leave digital traces or signal artifacts, impersonation involves live human beings producing entirely natural speech, rendering the detection of impersonation audio a challenging task. Thus, we propose a novel method that integrates speaker profiles into the process of impersonation audio detection. Speaker profiles are inherent characteristics that are challenging for impersonators to mimic accurately, such as speaker's age, job. We aim to leverage these features to extract discriminative information for detecting impersonation audio. Moreover, there is no large impersonated speech corpora available for quantitative study of impersonation impacts. To address this gap, we further design the first large-scale, diverse-speaker Chinese impersonation dataset, named ImPersonation Audio Detection (IPAD), to advance the community's research on impersonation audio detection. We evaluate several existing fake audio detection methods on our proposed dataset IPAD, demonstrating its necessity and the challenges. Additionally, our findings reveal that incorporating speaker profiles can significantly enhance the model's performance in detecting impersonation audio.

翻译：伪造音频检测是一个新兴的活跃课题。越来越多的文献致力于检测伪造语音，这些语音大多由文本转语音（TTS）或语音转换（VC）技术生成。然而，针对模仿行为的防御措施仍是一个研究不足的领域。模仿是一种伪造类型，涉及模仿者复制目标说话人的特定特征和说话风格。与TTS和VC通常留下数字痕迹或信号伪影不同，模仿涉及真人发出完全自然的语音，这使得模仿音频的检测成为一项具有挑战性的任务。因此，我们提出了一种新颖的方法，将说话人特征整合到模仿音频检测过程中。说话人特征是模仿者难以精确模仿的内在特征，例如说话人的年龄、职业。我们旨在利用这些特征来提取判别性信息，以检测模仿音频。此外，目前缺乏可用于定量研究模仿影响的大规模模仿语音语料库。为填补这一空白，我们进一步设计了首个大规模、多说话人的中文模仿数据集，命名为模仿音频检测数据集（IPAD），以推动学术界在模仿音频检测方面的研究。我们在我们提出的IPAD数据集上评估了几种现有的伪造音频检测方法，证明了其必要性和挑战性。此外，我们的研究结果表明，结合说话人特征可以显著提升模型在检测模仿音频方面的性能。