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 submissions.
翻译:用于言语嘲弄反措施的良好培训套件要求使用不同的TTS和VC假冒数据,但为目标演讲者进行TTS和VC假冒试验在技术上可能要求很高。本研究不是使用成熟的 TTS和VC系统,而是使用基于神经网络的vocoders对善意言词进行复制合成。输出数据可以用作假数据。为了更好地利用善意和假冒数据的对配方,本研究还引入了可纳入标准培训标准的对比特征损失。根据ASVspooof 2019逻辑访问训练集的善意试验,本研究以经验方式比较了以拟议方式创建的一些培训组合,使用了少数无偏向性声音的微调声音。多个测试组的结果显示了一些良好做法,例如利用目标域的善意数据和虚伪数据微调神经电动器等。研究结果还展示了对比特征损失的有效性。结合了最佳做法,经过培训的MEM在20-21级机密数据中实现了总体竞争性表现。