  Advances in deep learning have introduced a new wave of voice synthesis
tools, capable of producing audio that sounds as if spoken by a target speaker.
If successful, such tools in the wrong hands will enable a range of powerful
attacks against both humans and software systems (aka machines). This paper
documents efforts and findings from a comprehensive experimental study on the
impact of deep-learning based speech synthesis attacks on both human listeners
and machines such as speaker recognition and voice-signin systems. We find that
both humans and machines can be reliably fooled by synthetic speech and that
existing defenses against synthesized speech fall short. These findings
highlight the need to raise awareness and develop new protections against
synthetic speech for both humans and machines.
