Harnessing Indirect Training Data for End-to-End Automatic Speech Translation: Tricks of the Trade
Table 3: En–Fr results on AST Librispeech test set and En–Ro results on MuST-C test set with Bérard model. For ASR, we use the same AST subset of Librispeech as the ASR test set. ASR performance is measured by word error rate (WER; lower is better) and MT and AST performance is measured by BLEU (higher is better). MT, TTS, fine-tuning and pretraining help bridge the gap to a very strong cascade model.
['Task', 'Model', 'Data', 'En–Fr', 'En–Ro']
We first investigate techniques to improve end-to-end AST on Bérard model. Results for the En–Fr and En– The ASR and MT components we trained with additional data are very strong: the cascade model outperforms the vanilla end-to-end model by 8.2 BLEU (21.3 vs. 13.1) on the AST task. It is also important to note that our cascade baseline is greater than the best reported result on this task by 5.5 BLEU and even better than the previously reported oracle BLEU of 19.3 by 2 BLEU, where the gold transcript is passed to the translation system. The large vggtransformer outperforms other end-to-end approaches, now within 0.15 BLEU of the best cascade model.