Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Robust Front-End for Multi-Channel ASR using Flow-Based Density
  Estimation   For multi-channel speech recognition, speech enhancement techniques such as
denoising or dereverberation are conventionally applied as a front-end
processor. Deep learning-based front-ends using such techniques require aligned
clean and noisy speech pairs which are generally obtained via data simulation.
Recently, several joint optimization techniques have been proposed to train the
front-end without parallel data within an end-to-end automatic speech
recognition (ASR) scheme. However, the ASR objective is sub-optimal and
insufficient for fully training the front-end, which still leaves room for
improvement. In this paper, we propose a novel approach which incorporates
flow-based density estimation for the robust front-end using non-parallel clean
and noisy speech. Experimental results on the CHiME-4 dataset show that the
proposed method outperforms the conventional techniques where the front-end is
trained only with ASR objective.

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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: supr-con
2
The categories of this paper are: math
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The categories of this paper are: funct-an
9
The categories of this paper are: nlin
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The categories of this paper are: q-fin
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The categories of this paper are: quant-ph
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The categories of this paper are: hep-th
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The categories of this paper are: astro-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: hep-lat
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The categories of this paper are: plasm-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: nucl-ex
36
The categories of this paper are: cmp-lg