  We present a simple yet effective approach to build multilingual
speech-to-text (ST) translation by efficient transfer learning from pretrained
speech encoder and text decoder. Our key finding is that a minimalistic LNA
(LayerNorm and Attention) finetuning can achieve zero-shot crosslingual and
cross-modality transfer ability by only finetuning less than 10% of the
pretrained parameters. This enables effectively leveraging large pretrained
models with low training cost. Using wav2vec 2.0 for acoustic modeling, and
mBART for multilingual text generation, our approach advanced the new
state-of-the-art for 34 translation directions (and surpassing cascaded ST for
23 of them) on large-scale multilingual ST benchmark CoVoST 2 (+6.4 BLEU on
average across 15 En-X directions and +5.1 BLEU on average across 19 X-En
directions). Our approach demonstrates strong zero-shot performance in a
many-to-many multilingual model (+5.7 BLEU on average across 18 non-English
directions), making it an appealing approach for attaining high-quality speech
translation with improved parameter and data efficiency.
