“To enhance the effectiveness of fake audio detection techniques, researchers have developed mul-tiple datasets such as those for the ASVspoof and ADD challenges. These datasets typically focuson capturing non-emotional characteristics in speech, such as the identity of the speaker and theauthenticity of the content. However, they often overlook changes in the emotional state of theaudio, which is another crucial dimension affecting the authenticity of speech. Therefore, thisstudy reports our progress in developing such an emotion fake audio detection dataset involvingchanging emotion state of the origin audio named EmoFake. The audio samples in EmoFake aregenerated using open-source emotional voice conversion models, intended to simulate potentialemotional tampering scenarios in real-world settings. We conducted a series of benchmark ex-periments on this dataset, and the results show that even advanced fake audio detection modelstrained on the ASVspoof 2019 LA dataset and the ADD 2022 track 3.2 dataset face challengeswith EmoFake. The EmoFake is publicly available1 now.”