Synthetic voice and splicing audio clips have been generated to spoof Internet users and artificial intelligence (AI) technologies such as voice authentication. Existing research work treats spoofing countermeasures as a binary classification problem: bonafide vs. spoof. This paper extends the existing Res2Net by involving the recent Conformer block to further exploit the local patterns on acoustic features. Experimental results on ASVspoof 2019 database show that the proposed SE-Res2Net-Conformer architecture is able to improve the spoofing countermeasures performance for the logical access scenario. In addition, this paper also proposes to re-formulate the existing audio splicing detection problem. Instead of identifying the complete splicing segments, it is more useful to detect the boundaries of the spliced segments. Moreover, a deep learning approach can be used to solve the problem, which is different from the previous signal processing techniques.

本文提出了一种使用Res2Net和Conformer block相结合的架构来改进声纹鉴定的欺骗对策，同时也提出了一种新的音频拼接检测问题的深度学习方法，该方法可以检测拼接边界而不是完整的拼接片段。

利用SE-Res2Net-Conformer结构进行合成语音检测和音频剪辑检测