  Audio Adversarial Examples (AAE) represent specially created inputs meant to
trick Automatic Speech Recognition (ASR) systems into misclassification. The
present work proposes MP3 compression as a means to decrease the impact of
Adversarial Noise (AN) in audio samples transcribed by ASR systems. To this
end, we generated AAEs with the Fast Gradient Sign Method for an end-to-end,
hybrid CTC-attention ASR system. Our method is then validated by two objective
indicators: (1) Character Error Rates (CER) that measure the speech decoding
performance of four ASR models trained on uncompressed, as well as
MP3-compressed data sets and (2) Signal-to-Noise Ratio (SNR) estimated for both
uncompressed and MP3-compressed AAEs that are reconstructed in the time domain
by feature inversion. We found that MP3 compression applied to AAEs indeed
reduces the CER when compared to uncompressed AAEs. Moreover, feature-inverted
(reconstructed) AAEs had significantly higher SNRs after MP3 compression,
indicating that AN was reduced. In contrast to AN, MP3 compression applied to
utterances augmented with regular noise resulted in more transcription errors,
giving further evidence that MP3 encoding is effective in diminishing only AN.
