Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Dual Application of Speech Enhancement for Automatic Speech Recognition   In this work, we exploit speech enhancement for improving a recurrent neural
network transducer (RNN-T) based ASR system. We employ a dense convolutional
recurrent network (DCRN) for complex spectral mapping based speech enhancement,
and find it helpful for ASR in two ways: a data augmentation technique, and a
preprocessing frontend. In using it for ASR data augmentation, we exploit a KL
divergence based consistency loss that is computed between the ASR outputs of
original and enhanced utterances. In using speech enhancement as an effective
ASR frontend, we propose a three-step training scheme based on model
pretraining and feature selection. We evaluate our proposed techniques on a
challenging social media English video dataset, and achieve an average relative
improvement of 11.2% with speech enhancement based data augmentation, 8.3% with
enhancement based preprocessing, and 13.4% when combining both.

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The categories of this paper are: chao-dyn
5
The categories of this paper are: gr-qc
11
The categories of this paper are: hep-lat
36
The categories of this paper are: cmp-lg
33
The categories of this paper are: plasm-ph
24
The categories of this paper are: dg-ga
28
The categories of this paper are: alg-geom
23
The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph
12
The categories of this paper are: nucl-th
4
The categories of this paper are: cond-mat
35
The categories of this paper are: mtrl-th
3
The categories of this paper are: physics
9
The categories of this paper are: nlin
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The categories of this paper are: adap-org
27
The categories of this paper are: comp-gas