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.
Spoofing attack detection using the non-linear fusion of sub-band classifiers
INTERSPEECH 2020, 21st Annual Conference of the International Speech Communication Association, 25-29 October 2020, Shanghai, China (Virtual Conference)
© ISCA. Personal use of this material is permitted. The definitive version of this paper was published in INTERSPEECH 2020, 21st Annual Conference of the International Speech Communication Association, 25-29 October 2020, Shanghai, China (Virtual Conference) and is available at : http://dx.doi.org/10.21437/Interspeech.2020-1844
PERMALINK : https://www.eurecom.fr/publication/6270