FastAudio: A Learnable Audio Front-End For Spoof Speech Detection

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. The proposed FastAudio front-end is then tested with two popular back-ends to measure the performance on the Logical Access track of the ASVspoof 2019 dataset. The FastAudio front-end achieves a relative improvement of 29.7% when compared with fixed front-ends, outperforming all other learnable front-ends on this task.