Learning When to Translate for Streaming Speech
| Autor: | Qian Dong, Yaoming Zhu, Mingxuan Wang, Lei Li |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Computation and Language Audio and Speech Processing (eess.AS) FOS: Electrical engineering electronic engineering information engineering Computation and Language (cs.CL) Electrical Engineering and Systems Science - Audio and Speech Processing |
| DOI: | 10.48550/arxiv.2109.07368 |
| Popis: | How to find proper moments to generate partial sentence translation given a streaming speech input? Existing approaches waiting-and-translating for a fixed duration often break the acoustic units in speech, since the boundaries between acoustic units in speech are not even. In this paper, we propose MoSST, a simple yet effective method for translating streaming speech content. Given a usually long speech sequence, we develop an efficient monotonic segmentation module inside an encoder-decoder model to accumulate acoustic information incrementally and detect proper speech unit boundaries for the input in speech translation task. Experiments on multiple translation directions of the MuST-C dataset show that MoSST outperforms existing methods and achieves the best trade-off between translation quality (BLEU) and latency. Our code is available at https://github.com/dqqcasia/mosst. Comment: Accept to ACL 2022 main conference. 15 pages, 6 figures |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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