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

• URL: http://arxiv.org/abs/2309.08208v1
• Date: Fri, 15 Sep 2023 07:18:30 GMT
• Title: HM-Conformer: A Conformer-based audio deepfake detection system with hierarchical pooling and multi-level classification token aggregation methods
• Authors: Hyun-seo Shin, Jungwoo Heo, Ju-ho Kim, Chan-yeong Lim, Wonbin Kim, and Ha-Jin Yu
• Abstract summary: HM-Conformer is designed for sequence-to-sequence tasks. It can efficiently detect spoofing evidence by processing various sequence lengths and aggregating them. In experimental results, HM-Conformer achieved a 15.71% EER, showing competitive performance compared to recent systems.
• Score: 34.83806360076228
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: 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.

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