  Replay attack is one of the most effective and simplest voice spoofing
attacks. Detecting replay attacks is challenging, according to the Automatic
Speaker Verification Spoofing and Countermeasures Challenge 2021 (ASVspoof
2021), because they involve a loudspeaker, a microphone, and acoustic
conditions (e.g., background noise). One obstacle to detecting replay attacks
is finding robust feature representations that reflect the channel noise
information added to the replayed speech. This study proposes a feature
extraction approach that uses audio compression for assistance. Audio
compression compresses audio to preserve content and speaker information for
transmission. The missed information after decompression is expected to contain
content- and speaker-independent information (e.g., channel noise added during
the replay process). We conducted a comprehensive experiment with a few data
augmentation techniques and 3 classifiers on the ASVspoof 2021 physical access
(PA) set and confirmed the effectiveness of the proposed feature extraction
approach. To the best of our knowledge, the proposed approach achieves the
lowest EER at 22.71% on the ASVspoof 2021 PA evaluation set.
