Training Augmentation with Adversarial Examplesfor Robust Speech Recognition
Table 1: WER comparison on the Aurora-4 evaluation set with adversarial examples (AdvEx) (ϵ=0.3)
['[EMPTY]', 'A', 'B', 'C', 'D', 'AVG.']
Based on the results, ϵ=0.3 is chosen as the best perturbation weight to train the Aurora-4 model. The model trained on WSJ0m serves as the baseline. With ϵ=0.3, the augmented data training achieves 9.49% WER averaged across the four test sets, a 14.1% relative improvement over the baseline. For the test set with the highest WER on the baseline system, D, in which both noise and channel distortion are present, the proposed method reduced the WER by 18.6% relative. We also experimented with dropout training, and it gave only very small gains over the baseline.