Using Deep Learning for Detecting Spoofing Attacks on Speech Signals
Table 1: MLP classes output meanings.
['[BOLD] y0', '[BOLD] y1', '[BOLD] y2', '[BOLD] Meaning']
Preliminary experiments indicated that using only two classes – spoofing and human – as output led to poor perfomance in class S1. One hypothesis is that this could happen because S1 distinguishes from other attacks since it is based on a unit selection algorithm, which concatenates pieces of authentic signal to create a new audio. To deal with this, it was decided to drive the network training towards distinguishing S1 from the other spoofing attacks, increasing the relevance (on network performance) of detecting borders between pieces of authentic speech.