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Audio deepfake detection (ADD) is the task of detecting spoofing attacksgenerated by text-to-speech or voice conversion systems. Spoofing evidence,which helps to distinguish between spoofed and bona-fide utterances, mightexist either locally or globally in the input features. To capture these, theConformer, which consists of Transformers and CNN, possesses a suitablestructure. However, since the Conformer was designed for sequence-to-sequencetasks, its direct application to ADD tasks may be sub-optimal. To tackle thislimitation, we propose HM-Conformer by adopting two components: (1)Hierarchical pooling method progressively reducing the sequence length toeliminate duplicated information (2) Multi-level classification tokenaggregation method utilizing classification tokens to gather information fromdifferent blocks. Owing to these components, HM-Conformer can efficientlydetect spoofing evidence by processing various sequence lengths and aggregatingthem. In experimental results on the ASVspoof 2021 Deepfake dataset,HM-Conformer achieved a 15.71% EER, showing competitive performance compared torecent systems.

HM-Conformer: A Conformer-based audio deepfake detection system with hierarchical pooling and multi-level classification token aggregation methods