- Qianqian Dong, Yaoming Zhu, Mingxuan Wang, Lei Li. 2021. UniST: Unified End-to-end Model for Streaming and Non-streaming Speech Translation. Abstract: This paper presents a uniﬁed end-to-end framework for both streaming and non-streaming speech translation. While the training recipes for non-streaming speech translation have been mature, the recipes for streaming speech translation are yet to be built. In this work, we focus on developing a uniﬁed model (UniST) which supports streaming and non-streaming ST from the perspective of fundamental com-ponents, including training objective, attention mechanism and decoding policy. Experiments on the most popular speech-to-text translation benchmark dataset, MuST-C, show that UniST achieves signiﬁcant improvement for non-streaming ST, and a better-learned trade-off for BLEU score and latency metrics for streaming ST, compared with end-to-end baselines and the cascaded models. We will make our codes and evaluation tools publicly available.
