  A good training set for speech spoofing countermeasures requires diverse TTS
and VC spoofing attacks, but generating TTS and VC spoofed trials for a target
speaker may be technically demanding. Instead of using full-fledged TTS and VC
systems, this study uses neural-network-based vocoders to do copy-synthesis on
bona fide utterances. The output data can be used as spoofed data. To make
better use of pairs of bona fide and spoofed data, this study introduces a
contrastive feature loss that can be plugged into the standard training
criterion. On the basis of the bona fide trials from the ASVspoof 2019 logical
access training set, this study empirically compared a few training sets
created in the proposed manner using a few neural non-autoregressive vocoders.
Results on multiple test sets suggest good practices such as fine-tuning neural
vocoders using bona fide data from the target domain. The results also
demonstrated the effectiveness of the contrastive feature loss. Combining the
best practices, the trained CM achieved overall competitive performance. Its
EERs on the ASVspoof 2021 hidden subsets also outperformed the top-1 challenge
submission.
