How to Boost Anti-Spoofing with X-Vectors

With the development of speech synthesis or voice conversion, speech spoofing countermeasures are increasingly required for protecting automatic speaker verification system. In our daily life, if we are familiar with the speaker, we tend to seek her/his traits in our memory to distinguish between bona fide and spoofed speech of her/him. Speaker label can not be directly used to guide the training of anti-spoofing network because it is difficult to obtain in real scenes. Motivated by this, we use x-vectors to represent speaker information and propose two novel methods by introducing x-vectors on the acoustic feature and embedding level into the two mainstream anti-spoofing methods (LightCNN and SeNet). An attention module is also added on the embedding level for further improvement. Experimental results on the ASVspoof 2019 logical access (LA) database show that the best EER and mintDCF in our methods are 0.98% and 0.0294, outperforming state-of-the-art single systems as far as we know.