UniST: Unified End-to-end Model for Streaming and Non-streaming Speech Translation

• URL: http://arxiv.org/abs/2109.07368v1
• Date: Wed, 15 Sep 2021 15:22:10 GMT
• Title: UniST: Unified End-to-end Model for Streaming and Non-streaming Speech Translation
• Authors: Qianqian Dong, Yaoming Zhu, Mingxuan Wang, Lei Li
• Abstract summary: We develop a unified model (UniST) which supports streaming and non-streaming speech translation. Experiments on the most popular speech-to-text translation benchmark dataset, MuST-C, show that UniST achieves significant improvement for non-streaming ST.
• Score: 12.63410397982031
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents a unified end-to-end frame-work for both streaming and non-streamingspeech translation. While the training recipes for non-streaming speech translation have been mature, the recipes for streaming speechtranslation are yet to be built. In this work, wefocus on developing a unified model (UniST) which supports streaming and non-streaming ST from the perspective of fundamental components, 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.

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