Spoofing Attack Detection using the Non-linear Fusion of Sub-band Classifiers
This addresses the reliability issue in speaker verification systems against spoofing threats, representing an incremental improvement over existing ensemble methods.
The paper tackled the problem of spoofing attack detection in automatic speaker verification by proposing a non-linear fusion of simple sub-band classifiers, achieving superior performance that outperformed 46 out of 48 systems in the ASVspoof 2019 challenge.
The threat of spoofing can pose a risk to the reliability of automatic speaker verification. Results from the bi-annual 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.