Improving Stability in Simultaneous Speech Translation: A Revision-Controllable Decoding Approach

October 06, 2023 ยท Declared Dead ยท ๐Ÿ› Automatic Speech Recognition & Understanding

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Authors Junkun Chen, Jian Xue, Peidong Wang, Jing Pan, Jinyu Li arXiv ID 2310.04399 Category cs.CL: Computation & Language Citations 2 Venue Automatic Speech Recognition & Understanding Last Checked 5 months ago
Abstract
Simultaneous Speech-to-Text translation serves a critical role in real-time crosslingual communication. Despite the advancements in recent years, challenges remain in achieving stability in the translation process, a concern primarily manifested in the flickering of partial results. In this paper, we propose a novel revision-controllable method designed to address this issue. Our method introduces an allowed revision window within the beam search pruning process to screen out candidate translations likely to cause extensive revisions, leading to a substantial reduction in flickering and, crucially, providing the capability to completely eliminate flickering. The experiments demonstrate the proposed method can significantly improve the decoding stability without compromising substantially on the translation quality.
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