Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: MetricGAN+: An Improved Version of MetricGAN for Speech Enhancement   The discrepancy between the cost function used for training a speech
enhancement model and human auditory perception usually makes the quality of
enhanced speech unsatisfactory. Objective evaluation metrics which consider
human perception can hence serve as a bridge to reduce the gap. Our previously
proposed MetricGAN was designed to optimize objective metrics by connecting the
metric with a discriminator. Because only the scores of the target evaluation
functions are needed during training, the metrics can even be
non-differentiable. In this study, we propose a MetricGAN+ in which three
training techniques incorporating domain-knowledge of speech processing are
proposed. With these techniques, experimental results on the VoiceBank-DEMAND
dataset show that MetricGAN+ can increase PESQ score by 0.3 compared to the
previous MetricGAN and achieve state-of-the-art results (PESQ score = 3.15).

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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: solv-int
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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: plasm-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: atom-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: chao-dyn
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The categories of this paper are: hep-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: gr-qc
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The categories of this paper are: math-ph
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The categories of this paper are: nlin
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The categories of this paper are: funct-an