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Spoofing Attack Detection using the Non-linear Fusion of Sub-band Classifiers

Abstract : 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.
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Submitted on : Thursday, February 3, 2022 - 4:57:16 PM
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Hemlata Tak, Jose Patino, Andreas Nautsch, Nicholas Evans, Massimiliano Todisco. Spoofing Attack Detection using the Non-linear Fusion of Sub-band Classifiers. Interspeech 2020, Oct 2020, Shanghai, China. pp.1106-1110, ⟨10.21437/Interspeech.2020-1844⟩. ⟨hal-03555611⟩



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