  The recent advances in voice conversion (VC) and text-to-speech (TTS) make it
possible to produce natural sounding speech that poses threat to automatic
speaker verification (ASV) systems. To this end, research on spoofing
countermeasures has gained attention to protect ASV systems from such attacks.
While the advanced spoofing countermeasures are able to detect known nature of
spoofing attacks, they are not that effective under unknown attacks. In this
work, we propose a novel data augmentation technique using a-law and mu-law
based signal companding. We believe that the proposed method has an edge over
traditional data augmentation by adding small perturbation or quantization
noise. The studies are conducted on ASVspoof 2019 logical access corpus using
light convolutional neural network based system. We find that the proposed data
augmentation technique based on signal companding outperforms the
state-of-the-art spoofing countermeasures showing ability to handle unknown
nature of attacks.
