Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning When to Translate for Streaming Speech   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.

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The categories of this paper are: adap-org
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The categories of this paper are: plasm-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: patt-sol
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The categories of this paper are: comp-gas
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The categories of this paper are: hep-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: nucl-th
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The categories of this paper are: funct-an
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The categories of this paper are: chem-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: acc-phys
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The categories of this paper are: gr-qc
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The categories of this paper are: q-bio
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The categories of this paper are: stat
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The categories of this paper are: quant-ph