Harnessing Indirect Training Data for End-to-End Automatic Speech Translation: Tricks of the Trade
Table 4: Performance of the architectures we consider on the (En–Fr) AST Librispeech test set in the AST + MT data setting, which performed best in Table 3. The large vggtransformer outperforms other end-to-end approaches, now within 0.15 BLEU of the best cascade model.
['Architecture', 'BLEU']
In the AST + MT setting, we obtain BLEU of 19.87 with Bérard, 19.84 with vgglstm and 21.65 with vggtransformer, showing that these architectures are effective with additional data. vggtransformer in particular (obtaining the best end-to-end AST score) is on par with the vggtransformer cascade and −0.1 BLEU behind the vgglstm cascade.