  Recent work on adversarial examples has demonstrated that most natural inputs
can be perturbed to fool even state-of-the-art machine learning systems. But
does this happen for humans as well? In this work, we investigate: what
fraction of natural instances of speech can be turned into "illusions" which
either alter humans' perception or result in different people having
significantly different perceptions? We first consider the McGurk effect, the
phenomenon by which adding a carefully chosen video clip to the audio channel
affects the viewer's perception of what is said (McGurk and MacDonald, 1976).
We obtain empirical estimates that a significant fraction of both words and
sentences occurring in natural speech have some susceptibility to this effect.
We also learn models for predicting McGurk illusionability. Finally we
demonstrate that the Yanny or Laurel auditory illusion (Pressnitzer et al.,
2018) is not an isolated occurrence by generating several very different new
instances. We believe that the surprising density of illusionable natural
speech warrants further investigation, from the perspectives of both security
and cognitive science. Supplementary videos are available at:
https://www.youtube.com/playlist?list=PLaX7t1K-e_fF2iaenoKznCatm0RC37B_k.
