CoVoST 2: A Massively Multilingual Speech-to-Text Translation Corpus
Table 2: Test WER (CER) for monolingual ASR; test BLEU for bilingual MT, cascaded ST (C-ST) and end-to-end ST. All non-English ASR and all ST model encoders are pre-trained on English ASR. ⋆ We use CER and character-level BLEU on Chinese and Japanese (no word segmentation).
['[EMPTY]', 'ASR', 'X→En MT', 'X→En MT+Rev', 'X→En C-ST', 'X→En C-ST+Rev', 'X→En ST', 'En→X MT', 'En→X MT+Rev', 'En→X C-ST', 'En→X C-ST+Rev', 'En→X ST']
As expected, the quality of transcriptions and translations is very dependent on the amount of training data per language pair. The poor results obtained on low resource pairs can be improved by leveraging training data from the opposite direction for MT and C-ST. These results serve as baseline for the research community to improve upon, including methods such as multilingual training, self-supervised pre-training and semi-supervised learning.