Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Noise-robust Speech Recognition with 10 Minutes Unparalleled In-domain
  Data   Noise-robust speech recognition systems require large amounts of training
data including noisy speech data and corresponding transcripts to achieve
state-of-the-art performances in face of various practical environments.
However, such plenty of in-domain data is not always available in the real-life
world. In this paper, we propose a generative adversarial network to simulate
noisy spectrum from the clean spectrum (Simu-GAN), where only 10 minutes of
unparalleled in-domain noisy speech data is required as labels. Furthermore, we
also propose a dual-path speech recognition system to improve the robustness of
the system under noisy conditions. Experimental results show that the proposed
speech recognition system achieves 7.3% absolute improvement with simulated
noisy data by Simu-GAN over the best baseline in terms of word error rate
(WER).

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The categories of this paper are: chem-ph
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The categories of this paper are: physics
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The categories of this paper are: nucl-ex
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The categories of this paper are: math-ph
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The categories of this paper are: adap-org
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The categories of this paper are: quant-ph
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The categories of this paper are: patt-sol
6
The categories of this paper are: astro-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: hep-lat
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The categories of this paper are: ao-sci
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The categories of this paper are: cond-mat
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The categories of this paper are: dg-ga
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The categories of this paper are: hep-ph
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The categories of this paper are: q-fin
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The categories of this paper are: q-bio