Training Augmentation with Adversarial Examplesfor Robust Speech Recognition
Table 2: WER comparison on CHiME-4 single-channel track evaluation sets with adversarial examples (AdvEx) (ϵ=0.1).
['system', 'et05_simu BUS', 'et05_simu CAF', 'et05_simu PED', 'et05_simu STR', 'et05_simu  [BOLD] AVE.', 'et05_real BUS', 'et05_real CAF', 'et05_real PED', 'et05_real STR', 'et05_real  [BOLD] AVE.']
We see that within a reasonable range (ϵ<0.25) the proposed approach brings consistent gain. Relative WER reductions obtained on et05_real and et05_simu sets were 5.7% and 5.3%, respectively. The proposed approach was able to bring consistent improvements for all types of noises, whether in simulated or real environments.