# Improving pronunciation assessment via ordinal regression with anchored reference samples

arXiv: https://arxiv.org/abs/2010.13339 · HTML: https://mlsanity.com/paper/2010.13339/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Oct 2020.

## Built on by (most cited first, 2 of 2)

- [End-to-End Word-Level Pronunciation Assessment with MASK Pre-training](https://mlsanity.com/paper/2306.02682/) (2023)
- [Ordinal Regression via Binary Preference vs Simple Regression: Statistical and Experimental Perspectives](https://mlsanity.com/paper/2207.02454/) (2022)

## Abstract (arXiv)

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.