@inproceedings{1145,
  author = {Quchen Fu and Zhongwei Teng and Jules White and Maria Powell and Douglas Schmidt},
  title = {FastAudio: A Learnable Audio Front-End For Spoof Speech Detection},
  abstract = {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.},
  year = {2022},
  journal = {2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages = {3693-3697},
  month = {05/2022},
  publisher = {IEEE},
  address = {Singapore, Singapore},
  issn = {2379-190X},
  isbn = {978-1-6654-0540-9},
  url = {https://ieeexplore.ieee.org/document/9746722},
  doi = {10.1109/ICASSP43922.2022.9746722},
}