  This paper describes our submitted systems to the 2022 ADD challenge withing
the tracks 1 and 2. Our approach is based on the combination of a pre-trained
wav2vec2 feature extractor and a downstream classifier to detect spoofed audio.
This method exploits the contextualized speech representations at the different
transformer layers to fully capture discriminative information. Furthermore,
the classification model is adapted to the application scenario using different
data augmentation techniques. We evaluate our system for audio synthesis
detection in both the ASVspoof 2021 and the 2022 ADD challenges, showing its
robustness and good performance in realistic challenging environments such as
telephonic and audio codec systems, noisy audio, and partial deepfakes.
