  This paper introduces a novel adversarial algorithm for attacking the
state-of-the-art speech-to-text systems, namely DeepSpeech, Kaldi, and Lingvo.
Our approach is based on developing an extension for the conventional
distortion condition of the adversarial optimization formulation using the
Cram\`er integral probability metric. Minimizing over this metric, which
measures the discrepancies between original and adversarial samples'
distributions, contributes to crafting signals very close to the subspace of
legitimate speech recordings. This helps to yield more robust adversarial
signals against playback over-the-air without employing neither costly
expectation over transformation operations nor static room impulse response
simulations. Our approach outperforms other targeted and non-targeted
algorithms in terms of word error rate and sentence-level-accuracy with
competitive performance on the crafted adversarial signals' quality. Compared
to seven other strong white and black-box adversarial attacks, our proposed
approach is considerably more resilient against multiple consecutive playbacks
over-the-air, corroborating its higher robustness in noisy environments.

Sound;Machine Learning;Audio and Speech Processing