Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Using multi-task learning to improve the performance of acoustic-to-word
  and conventional hybrid models   Acoustic-to-word (A2W) models that allow direct mapping from acoustic signals
to word sequences are an appealing approach to end-to-end automatic speech
recognition due to their simplicity. However, prior works have shown that
modelling A2W typically encounters issues of data sparsity that prevent
training such a model directly. So far, pre-training initialization is the only
approach proposed to deal with this issue. In this work, we propose to build a
shared neural network and optimize A2W and conventional hybrid models in a
multi-task manner. Our results show that training an A2W model is much more
stable with our multi-task model without pre-training initialization, and
results in a significant improvement compared to a baseline model. Experiments
also reveal that the performance of a hybrid acoustic model can be further
improved when jointly training with a sequence-level optimization criterion
such as acoustic-to-word.

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The categories of this paper are: atom-ph
3
The categories of this paper are: physics
14
The categories of this paper are: math-ph
27
The categories of this paper are: comp-gas
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The categories of this paper are: adap-org
28
The categories of this paper are: alg-geom
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The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an
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The categories of this paper are: dg-ga
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The categories of this paper are: solv-int
5
The categories of this paper are: gr-qc
16
The categories of this paper are: stat
4
The categories of this paper are: cond-mat
12
The categories of this paper are: nucl-th
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The categories of this paper are: q-bio
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The categories of this paper are: astro-ph