Although neural machine translation (NMT) has achieved impressive progress recently, it is usually trained on the clean parallel data set and hence cannot work well when the input sentence is the production of the automatic speech recognition (ASR) system due to the enormous errors in the source. To solve this problem, we propose a simple but effective method to improve the robustness of NMT in the case of speech translation. We simulate the noise existing in the realistic output of the ASR system and inject them into the clean parallel data so that NMT can work under similar word distributions during training and testing. Besides, we also incorporate the Chinese Pinyin feature which is easy to get in speech translation to further improve the translation performance. Experiment results show that our method has a more stable performance and outperforms the baseline by an average of 3.12 BLEU on multiple noisy test sets, even while achieves a generalization improvement on the WMT'17 Chinese-English test set.

提出了一种简单且有效的方法来改进神经机器翻译在语音翻译中的稳健性，通过在干净的平行数据集中注入真实输出中存在的噪声以及结合拼音特征，使NMT可以在类似的单词分布下进行训练和测试，实验结果表明，该方法在多个噪声测试集上的稳定性表现优异，并在WMT'17中英测试集上取得了泛化性能的提高。

提高语音翻译的稳健性