  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.
