End-to-End Speech Translation with Knowledge Distillation
Table 3: MT and ST results on TED English-Chinese test.
['TED', 'MT', 'Pipeline', 'End-to-end', 'KD']
We also conduct experiments on English-Chinese to verify our methods. Pipeline model combines both the ASR (WER is 18.2%) and MT models. It is difficult to train end-to-end ST model from random initialization parameters, for the reordering between dissimilar language pairs is difficult to align with frame based speech representations. The end-to-end ST model here is pretrained with ASR. With knowledge distillation, it can obtain significant simprovements, proving the generality of our method. Although end-to-end ST does not outperform pipeline system, it shows the potential to implement a compact end-to-end model even on dissimilar language pairs.