Improving pronunciation assessment via ordinal regression with anchored reference samples
This work addresses weaknesses in pronunciation assessment for computer-assisted language learning, offering an incremental improvement over existing methods.
The paper tackled the problem of sentence-level pronunciation assessment in language learning by proposing new statistical features and an ordinal regression method, achieving a 26.9% relative improvement in correlation with human ratings and performance at or above human parity.
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.