Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improving pronunciation assessment via ordinal regression with anchored
  reference samples   Sentence level pronunciation assessment is important for Computer Assisted
Language Learning (CALL). Traditional speech pronunciation assessment, based on
the Goodness of Pronunciation (GOP) algorithm, has some weakness in assessing a
speech utterance: 1) Phoneme GOP scores cannot be easily translated into a
sentence score with a simple average for effective assessment; 2) The rank
ordering information has not been well exploited in GOP scoring for delivering
a robust assessment and correlate well with a human rater's evaluations. In
this paper, we propose two new statistical features, average GOP (aGOP) and
confusion GOP (cGOP) and use them to train a binary classifier in Ordinal
Regression with Anchored Reference Samples (ORARS). When the proposed approach
is tested on Microsoft mTutor ESL Dataset, a relative improvement of Pearson
correlation coefficient of 26.9% is obtained over the conventional GOP-based
one. The performance is at a human-parity level or better than human raters.

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The categories of this paper are: supr-con
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The categories of this paper are: quant-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: q-bio
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The categories of this paper are: stat
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The categories of this paper are: hep-th
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The categories of this paper are: bayes-an
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The categories of this paper are: mtrl-th
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The categories of this paper are: cmp-lg
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The categories of this paper are: hep-ph
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The categories of this paper are: solv-int
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The categories of this paper are: funct-an
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The categories of this paper are: physics
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The categories of this paper are: math
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The categories of this paper are: atom-ph
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The categories of this paper are: econ