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A reliable voice anti-spoofing countermeasure system needs to robustlyprotect automatic speaker verification (ASV) systems in various kinds ofspoofing scenarios. However, the performance of countermeasure systems could bedegraded by channel effects and codecs. In this paper, we show that using thelow-frequency subbands of signals as input can mitigate the negative impactintroduced by codecs on the countermeasure systems. To validate this, two typesof low-pass filters with different cut-off frequencies are applied tocountermeasure systems, and the equal error rate (EER) is reduced by up to 25%relatively. In addition, we propose a deep learning based bandwidth extensionapproach to further improve the detection accuracy. Recent studies show thatthe error rate of countermeasure systems increase dramatically when the silencepart is removed by Voice Activity Detection (VAD), our experimental resultsshow that the filtering and bandwidth extension approaches are also effectiveunder the codec condition when VAD is applied.

Low Pass Filtering and Bandwidth Extension for Robust Anti-spoofing Countermeasure Against Codec Variabilities