Audio deepfake detection (ADD) is the task of detecting spoofing attacks
generated by text-to-speech or voice conversion systems. Spoofing evidence,
which helps to distinguish between spoofed and bona-fide utterances, might
exist either locally or globally in the input features. To capture these, the
Conformer, which consists of Transformers and CNN, possesses a suitable
structure. However, since the Conformer was designed for sequence-to-sequence
tasks, its direct application to ADD tasks may be sub-optimal. To tackle this
limitation, we propose HM-Conformer by adopting two components: (1)
Hierarchical pooling method progressively reducing the sequence length to
eliminate duplicated information (2) Multi-level classification token
aggregation method utilizing classification tokens to gather information from
different blocks. Owing to these components, HM-Conformer can efficiently
detect spoofing evidence by processing various sequence lengths and aggregating
them. In experimental results on the ASVspoof 2021 Deepfake dataset,
HM-Conformer achieved a 15.71% EER, showing competitive performance compared to
recent systems.

音频深度伪造检测是检测由文本转语音或语音转换系统生成的欺骗攻击的任务。为了解决序列长度和信息聚合的问题，本文提出了 HM-Conformer，采用分层池化方法和多级分类令牌聚合方法，通过处理不同的序列长度并聚合它们，有效地检测欺骗证据。在 ASVspoof 2021 Deepfake 数据集上的实验结果显示，HM-Conformer 的等错误率为 15.71％，与最近的系统相比，表现竞争性能。