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

Hemlata Tak (1) , Jose Patino (1) , Andreas Nautsch (1) , Nicholas Evans (1) , Massimiliano Todisco (1)

1 Eurecom [Sophia Antipolis]

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.

Mots clés: Spoofing; Sub-band countermeasures; Presentation attack detection; ASVspoof; Speaker verification

Domaines: Ingénierie assistée par ordinateur

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⟩