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.

翻译：音频深度伪造检测（ADD）是识别由文本到语音或语音转换系统生成的欺骗攻击的任务。有助于区分伪造语音与真实语音的欺骗证据，可能存在于输入特征的局部或全局范围内。为捕捉这些特征，由Transformer和CNN构成的Conformer具有适宜的结构。然而，由于Conformer是为序列到序列任务设计的，直接应用于ADD任务可能并非最优。为解决这一局限，我们提出HM-Conformer，采用两个组件：（1）层次化池化方法，逐步缩短序列长度以消除冗余信息；（2）多级分类令牌聚合方法，利用分类令牌从不同模块收集信息。得益于这些组件，HM-Conformer能够通过处理不同序列长度并进行聚合，高效检测欺骗证据。在ASVspoof 2021深度伪造数据集上的实验结果显示，HM-Conformer实现了15.71%的等错误率，展现出与近期系统相比具有竞争力的性能。