  In this work, we investigate multilingual speech Pre-Trained models (PTMs)
for Audio deepfake detection (ADD). We hypothesize that multilingual PTMs
trained on large-scale diverse multilingual data gain knowledge about diverse
pitches, accents, and tones, during their pre-training phase and making them
more robust to variations. As a result, they will be more effective for
detecting audio deepfakes. To validate our hypothesis, we extract
representations from state-of-the-art (SOTA) PTMs including monolingual,
multilingual as well as PTMs trained for speaker and emotion recognition, and
evaluated them on ASVSpoof 2019 (ASV), In-the-Wild (ITW), and DECRO benchmark
databases. We show that representations from multilingual PTMs, with simple
downstream networks, attain the best performance for ADD compared to other PTM
representations, which validates our hypothesis. We also explore the
possibility of fusion of selected PTM representations for further improvements
in ADD, and we propose a framework, MiO (Merge into One) for this purpose. With
MiO, we achieve SOTA performance on ASV and ITW and comparable performance on
DECRO with current SOTA works.
