Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Predicting score distribution to improve non-intrusive speech quality
  estimation   Deep noise suppressors (DNS) have become an attractive solution to remove
background noise, reverberation, and distortions from speech and are widely
used in telephony/voice applications. They are also occasionally prone to
introducing artifacts and lowering the perceptual quality of the speech.
Subjective listening tests that use multiple human judges to derive a mean
opinion score (MOS) are a popular way to measure these models' performance.
Deep neural network based non-intrusive MOS estimation models have recently
emerged as a popular cost-efficient alternative to these tests. These models
are trained with only the MOS labels, often discarding the secondary statistics
of the opinion scores. In this paper, we investigate several ways to integrate
the distribution of opinion scores (e.g. variance, histogram information) to
improve the MOS estimation performance. Our model is trained on a corpus of
419K denoised samples by 320 different DNS models and model variations and
evaluated on 18K test samples from DNSMOS. We show that with very minor
modification of a single task MOS estimation pipeline, these freely available
labels can provide up to a 0.016 RMSE and 1% SRCC improvement.

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The categories of this paper are: physics
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The categories of this paper are: atom-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: patt-sol
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The categories of this paper are: dg-ga
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The categories of this paper are: chao-dyn
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The categories of this paper are: comp-gas
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The categories of this paper are: nlin
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The categories of this paper are: econ
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The categories of this paper are: plasm-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: math-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: stat
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The categories of this paper are: hep-th