  The past few years have witnessed the significant advances of speech
synthesis and voice conversion technologies. However, such technologies can
undermine the robustness of broadly implemented biometric identification models
and can be harnessed by in-the-wild attackers for illegal uses. The ASVspoof
challenge mainly focuses on synthesized audios by advanced speech synthesis and
voice conversion models, and replay attacks. Recently, the first Audio Deep
Synthesis Detection challenge (ADD 2022) extends the attack scenarios into more
aspects. Also ADD 2022 is the first challenge to propose the partially fake
audio detection task. Such brand new attacks are dangerous and how to tackle
such attacks remains an open question. Thus, we propose a novel framework by
introducing the question-answering (fake span discovery) strategy with the
self-attention mechanism to detect partially fake audios. The proposed fake
span detection module tasks the anti-spoofing model to predict the start and
end positions of the fake clip within the partially fake audio, address the
model's attention into discovering the fake spans rather than other shortcuts
with less generalization, and finally equips the model with the discrimination
capacity between real and partially fake audios. Our submission ranked second
in the partially fake audio detection track of ADD 2022.
