1 research outputs found
Improving the Robustness of Speech Translation
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