Before revision:  With this approach, we built a multilingual speech-to-text translation model with pretrained audio encoder ( wav2vec ) and multilingual text decoder (mBART), which achieves new state-of-the-art on CoVoST 2 ST benchmark 1 for English into 15 languages as well as 6 Romance languages into English with on average + 2.8 BLEU and + 3.9 BLEU , respectively. On low-resource languages (with less than 10 hours training data), our approach significantly improves the quality of speech-to-text translation with +9.0 BLEU on Portuguese-English and +5.2 BLEU on Dutch-English .
After revision:  With this approach, we built a multilingual speech-to-text translation model with pretrained audio encoder ( wav2vec ) and multilingual text decoder (mBART), which achieves new state-of-the-art on CoVoST 2 ST benchmark 1 for English into 15 languages as well as 6 Romance languages into English with on average + 2.8 BLEU and + 4.3 BLEU on average for 19 X-En directions). Our approach demonstrates strong performance in both zero-shot crosslingual transfer and zero-shot pair-wise translation in a many-to-many multilingual model, making it an appealing approach for attaining high-quality speech translation with low training cost and improved data efficiency .