Hear No Evil: Towards Adversarial Robustness of Automatic Speech Recognition via Multi-Task Learning
Journal: arXiv (Cornell University)
Year: 2022

As automatic speech recognition (ASR) systems are now being widely deployed in the wild, the increasing threat of adversarial attacks raises serious questions about the security and reliability of using such systems. On the other hand, multi-task learning (MTL) has shown success in training models that can resist adversarial attacks in the computer vision domain. In this work, we investigate the impact of performing such multi-task learning on the adversarial robustness of ASR models in the spee