  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.
