  Automatic speech recognition (ASR) systems are ubiquitously present in our
daily devices. They are vulnerable to adversarial attacks, where manipulated
input samples fool the ASR system's recognition. While adversarial examples for
various English ASR systems have already been analyzed, there exists no
inter-language comparative vulnerability analysis. We compare the attackability
of a German and an English ASR system, taking Deepspeech as an example. We
investigate if one of the language models is more susceptible to manipulations
than the other. The results of our experiments suggest statistically
significant differences between English and German in terms of computational
effort necessary for the successful generation of adversarial examples. This
result encourages further research in language-dependent characteristics in the
robustness analysis of ASR.
