Sequential Randomized Smoothing for Adversarially Robust Speech Recognition. (arXiv:2112.03000v2 [cs.CL] UPDATED)

While Automatic Speech Recognition has been shown to be vulnerable to
adversarial attacks, defenses against these attacks are still lagging.
Existing, naive defenses can be partially broken with an adaptive attack. In
classification tasks, the Randomized Smoothing paradigm has been shown to be
effective at defending models. However, it is difficult to apply this paradigm
to ASR tasks, due to their complexity and the sequential nature of their
outputs. Our paper overcomes some of these challenges by leveraging
speech-specific tools …

arxiv speech speech recognition