Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Vectorization of hypotheses and speech for faster beam search in encoder
  decoder-based speech recognition   Attention-based encoder decoder network uses a left-to-right beam search
algorithm in the inference step. The current beam search expands hypotheses and
traverses the expanded hypotheses at the next time step. This traversal is
implemented using a for-loop program in general, and it leads to speed down of
the recognition process. In this paper, we propose a parallelism technique for
beam search, which accelerates the search process by vectorizing multiple
hypotheses to eliminate the for-loop program. We also propose a technique to
batch multiple speech utterances for off-line recognition use, which reduces
the for-loop program with regard to the traverse of multiple utterances. This
extension is not trivial during beam search unlike during training due to
several pruning and thresholding techniques for efficient decoding. In
addition, our method can combine scores of external modules, RNNLM and CTC, in
a batch as shallow fusion. We achieved 3.7 x speedup compared with the original
beam search algorithm by vectoring hypotheses, and achieved 10.5 x speedup by
further changing processing unit to GPU.

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The categories of this paper are: chao-dyn
29
The categories of this paper are: funct-an
32
The categories of this paper are: atom-ph
13
The categories of this paper are: quant-ph
27
The categories of this paper are: comp-gas
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The categories of this paper are: adap-org
35
The categories of this paper are: mtrl-th
11
The categories of this paper are: hep-lat
25
The categories of this paper are: patt-sol
30
The categories of this paper are: q-alg
33
The categories of this paper are: plasm-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: q-fin
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The categories of this paper are: cond-mat
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The categories of this paper are: bayes-an
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The categories of this paper are: chem-ph