ESPnet-ST: All-in-One Speech Translation Toolkit
Table 2: BLEU of ST systems on Fisher-CallHome Spanish corpus. ♣Implemented w/ ESPnet. ♢w/ SpecAugment.
['Model', 'Model', 'Es → En Fisher', 'Es → En Fisher', 'Es → En Fisher', 'Es → En CallHome', 'Es → En CallHome']
It is worth noting that we did not use any additional data resource. Both MTL and transfer learning improved the performance of vanilla Transformer. Moreover, the total training/inference time is much shorter since our E2E-ST models are based on the BPE1k unit rather than characters. Multilingual end-to-end speech translation. In Proceedings of 2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU 2019), pp. 570–577.