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Synthetic voice and splicing audio clips have been generated to spoofInternet users and artificial intelligence (AI) technologies such as voiceauthentication. Existing research work treats spoofing countermeasures as abinary classification problem: bonafide vs. spoof. This paper extends theexisting Res2Net by involving the recent Conformer block to further exploit thelocal patterns on acoustic features. Experimental results on ASVspoof 2019database show that the proposed SE-Res2Net-Conformer architecture is able toimprove the spoofing countermeasures performance for the logical accessscenario.  In addition, this paper also proposes to re-formulate the existing audiosplicing detection problem. Instead of identifying the complete splicingsegments, 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 isdifferent from the previous signal processing techniques.

Synthetic Voice Detection and Audio Splicing Detection using SE-Res2Net-Conformer Architecture