This paper explores the use of adversarial examples in training speech recognition systems to increase robustness of deep neural network acoustic models. During training, the fast gradient sign method is used to generate adversarial examples augmenting the original training data. Different from conventional data augmentation based on data transformations, the examples are dynamically generated based on current acoustic model parameters. We assess the impact of adversarial data augmentation in experiments on the Aurora-4 and CHiME-4 single-channel tasks, showing improved robustness against noise and channel variation. Further improvement is obtained when combining adversarial examples with teacher/student training, leading to a 23% relative word error rate reduction on Aurora-4.

本文探讨了使用对抗性样本在训练语音识别系统中以增强深度神经网络声学模型的鲁棒性，结果表明相对于传统的数据通过数据转换技术实现的增强，动态利用当前声学模型参数生成对抗性样本的方法在Aurora-4和CHiME-4单通道实验中具有显著的改善表现。此外，将对抗性样本与老师/学生培训相结合，可以进一步提高识别准确率，Aurora-4相对误差率降低了23%。

使用对抗样本进行训练增强的鲁棒语音识别