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

Improving the Robustness of Speech Translation

In many applications, such as development and testing of image processing
algorithms, it is often necessary to simulate images containing realistic noise
from solid-state photosensors. A high-level model of CCD and CMOS photosensors
based on a literature review is formulated in this paper. The model includes
photo-response non-uniformity, photon shot noise, dark current Fixed Pattern
Noise, dark current shot noise, offset Fixed Pattern Noise, source follower
noise, sense node reset noise, and quantisation noise. The model also includes
voltage-to-voltage, voltage-to-electrons, and analogue-to-digital converter
non-linearities. The formulated model can be used to create synthetic images
for testing and validation of image processing algorithms in the presence of
realistic images noise. An example of the simulated CMOS photosensor and a
comparison with a custom-made CMOS hardware sensor is presented. Procedures for
characterisation from both light and dark noises are described. Experimental
results that confirm the validity of the numerical model are provided. The
paper addresses the issue of the lack of comprehensive high-level photosensor
models that enable engineers to simulate realistic effects of noise on the
images obtained from solid-state photosensors.

该论文提出了一个基于 CCD 和 CMOS 光传感器的高层次模型，并描述了使用该模型进行图片合成和噪声测试的过程，实验证明了该模型的有效性和应用范围。