# Enhancing GOP in CTC-Based Mispronunciation Detection with Phonological Knowledge

arXiv: https://arxiv.org/abs/2506.02080 · HTML: https://mlsanity.com/paper/2506.02080/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 35 references that are in the map.
- First posted on arXiv 2 Jun 2025.

## Builds on (most cited first, 10 of 10)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [Simple and Effective Zero-shot Cross-lingual Phoneme Recognition](https://mlsanity.com/paper/2109.11680/) (2021)
- [speechocean762: An Open-Source Non-native English Speech Corpus For Pronunciation Assessment](https://mlsanity.com/paper/2104.01378/) (2021)
- [Context-aware Goodness of Pronunciation for Computer-Assisted Pronunciation Training](https://mlsanity.com/paper/2008.08647/) (2020)
- [CTC Variations Through New WFST Topologies](https://mlsanity.com/paper/2110.03098/) (2021)
- [A Hierarchical Context-aware Modeling Approach for Multi-aspect and Multi-granular Pronunciation Assessment](https://mlsanity.com/paper/2305.18146/) (2023)
- [Self-Supervised Models for Phoneme Recognition: Applications in Children's Speech for Reading Learning](https://mlsanity.com/paper/2503.04710/) (2025)

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

- [Rubric-Guided Fine-tuning of SpeechLLMs for Multi-Aspect, Multi-Rater L2 Reading-Speech Assessment](https://mlsanity.com/paper/2603.16889/) (2026)
- [A Finetuned SpeechLLM for Joint Multi-Granular L2 Assessment and Natural-Language Rationales](https://mlsanity.com/paper/2606.09470/) (2026)

## Abstract (arXiv)

Computer-Assisted Pronunciation Training (CAPT) systems employ automatic measures of pronunciation quality, such as the goodness of pronunciation (GOP) metric. GOP relies on forced alignments, which are prone to labeling and segmentation errors due to acoustic variability. While alignment-free methods address these challenges, they are computationally expensive and scale poorly with phoneme sequence length and inventory size. To enhance efficiency, we introduce a substitution-aware alignment-free GOP that restricts phoneme substitutions based on phoneme clusters and common learner errors. We evaluated our GOP on two L2 English speech datasets, one with child speech, My Pronunciation Coach (MPC), and SpeechOcean762, which includes child and adult speech. We compared RPS (restricted phoneme substitutions) and UPS (unrestricted phoneme substitutions) setups within alignment-free methods, which outperformed the baseline. We discuss our results and outline avenues for future research.