Spoofing Attack Detection Using the Non-Linear Fusion of Sub-Band Classifiers

Hemlata Tak, Jose Patino, Andreas Nautsch, Nicholas Evans, Massimiliano Todisco

The threat of spoofing can pose a risk to the reliability of automatic speaker verification. Results from the biannual ASVspoof evaluations show that effective countermeasures demand front-ends designed specifically for the detection of spoofing artefacts. Given the diversity in spoofing attacks, ensemble methods are particularly effective. The work in this paper shows that a bank of very simple classifiers, each with a front-end tuned to the detection of different spoofing attacks and combined at the score level through non-linear fusion, can deliver superior performance than more sophisticated ensemble solutions that rely upon complex neural network architectures. Our comparatively simple approach outperforms all but 2 of the 48 systems submitted to the logical access condition of the most recent ASVspoof 2019 challenge.

 DOI: 10.21437/Interspeech.2020-1844

Cite as: Tak, H., Patino, J., Nautsch, A., Evans, N., Todisco, M. (2020) Spoofing Attack Detection Using the Non-Linear Fusion of Sub-Band Classifiers. Proc. Interspeech 2020, 1106-1110, DOI: 10.21437/Interspeech.2020-1844.

  author={Hemlata Tak and Jose Patino and Andreas Nautsch and Nicholas Evans and Massimiliano Todisco},
  title={{Spoofing Attack Detection Using the Non-Linear Fusion of Sub-Band Classifiers}},
  booktitle={Proc. Interspeech 2020},