Many datasets have been designed to further the development of fake audio detection, such as datasets of the ASVspoof and ADD challenges. However, these datasets do not consider a situation that the emotion of the audio has been changed from one to another, while other information (e.g. speaker identity and content) remains the same. Changing the emotion of an audio can lead to semantic changes. Speech with tampered semantics may pose threats to people's lives. Therefore, this paper reports our progress in developing such an emotion fake audio detection dataset involving changing emotion state of the origin audio named EmoFake. The fake audio in EmoFake is generated by open source emotion voice conversion models. Furthermore, we proposed a method named Graph Attention networks using Deep Emotion embedding (GADE) for the detection of emotion fake audio. Some benchmark experiments are conducted on this dataset. The results show that our designed dataset poses a challenge to the fake audio detection model trained with the LA dataset of ASVspoof 2019. The proposed GADE shows good performance in the face of emotion fake audio.

翻译：为促进伪造音频检测技术的发展，已有许多数据集被构建，例如ASVspoof和ADD挑战赛中的数据集。然而，现有数据集未考虑音频情感从一种状态转换为另一种状态，而其他信息（如说话人身份和内容）保持不变的情形。改变音频情感可能导致语义变化，篡改语义的语音可能对人们的生活构成威胁。因此，本文报告了我们在构建涉及原始音频情感状态改变的伪造音频检测数据集（命名为EmoFake）方面的进展。该数据集中的伪造音频由开源情感语音转换模型生成。此外，我们提出了一种基于深度情感嵌入的图注意力网络（GADE）方法，用于情感伪造音频检测。在该数据集上进行了若干基准实验，结果表明我们设计的数据集对使用ASVspoof 2019的LA数据集训练的伪造音频检测模型构成了挑战。所提出的GADE方法在面对情感伪造音频时表现出良好的性能。