  End-to-end speech translation (ST), which directly translates from source
language speech into target language text, has attracted intensive attentions
in recent years. Compared to conventional pipeline systems, end-to-end ST
models have advantages of lower latency, smaller model size and less error
propagation. However, the combination of speech recognition and text
translation in one model is more difficult than each of these two tasks. In
this paper, we propose a knowledge distillation approach to improve ST model by
transferring the knowledge from text translation model. Specifically, we first
train a text translation model, regarded as a teacher model, and then ST model
is trained to learn output probabilities from teacher model through knowledge
distillation. Experiments on English- French Augmented LibriSpeech and
English-Chinese TED corpus show that end-to-end ST is possible to implement on
both similar and dissimilar language pairs. In addition, with the instruction
of teacher model, end-to-end ST model can gain significant improvements by over
3.5 BLEU points.
