Towards Robust Speech-to-Text Adversarial Attack

Abstract

This paper introduces a novel adversarial algorithm for attacking the advanced speech-to-text transcription systems. Our proposed approach is based on developing an extension for the conventional distortion condition of the general adversarial optimization formulation using the Cramér integral probability metric. Minimizing over such a metric contributes to crafting signals very close to the subspace of legitimate speech recordings. That helps yield more robust adversarial signals against over-the-air playbacks without employing neither costly expectation over transformations nor static room impulse response simulations. Our approach considerably outperforms other targeted and non-targeted algorithms in terms of word error rate and sentence-level accuracy. Furthermore compared to seven other strong white and black-box adversarial attacks, our proposed approach is considerably more resilient against multiple consecutive over-the-air playbacks, corroborating its higher robustness in noisy environments.