A Comparative Study on End-to-End Speech to Text Translation
Table 7: Comparison of test sets with the literature. 1: the evaluation is without punctuation. To be comparable with other works, we note that the LibriSpeech results in this table are case-insensitive Bleu computed using multi-bleu.pl script [23].
['[BOLD] Method', '[BOLD] En→De', '[BOLD] En→Fr']
We use our best setup which is a direct model using an adapter on top of pre-training for the comparison in this table. On the IWSLT test set, our model outperforms the winner of the IWSLT2018 evaluation campaign by 0.67% in Bleu. On the LibriSpeech test set, our model outperforms both the LSTM-based and the transformer models and slightly behind the knowledge distillation method.