Spoof speech can be used to try and fool speaker verification systems that determine the identity of the speaker based on voice characteristics. This paper compares popular learnable front-ends on this task. We categorize the front-ends by defining two generic architectures and then analyze the filtering stages of both types in terms of learning constraints. We pro-pose replacing fixed filterbanks with a learnable layer that can better adapt to anti-spoofing tasks.

2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

IEEE

978-1-6654-0540-9

10.1109/ICASSP43922.2022.9746722