# Context-aware Goodness of Pronunciation for Computer-Assisted Pronunciation Training

arXiv: https://arxiv.org/abs/2008.08647 · HTML: https://mlsanity.com/paper/2008.08647/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2020.

## Built on by (most cited first, 26 of 26)

- [Transformer-Based Multi-Aspect Multi-Granularity Non-Native English Speaker Pronunciation Assessment](https://mlsanity.com/paper/2205.03432/) (2022)
- [A Full Text-Dependent End to End Mispronunciation Detection and Diagnosis with Easy Data Augmentation Techniques](https://mlsanity.com/paper/2104.08428/) (2021)
- [Leveraging Allophony in Self-Supervised Speech Models for Atypical Pronunciation Assessment](https://mlsanity.com/paper/2502.07029/) (2025)
- [Hierarchical Pronunciation Assessment with Multi-Aspect Attention](https://mlsanity.com/paper/2211.08102/) (2022)
- [Multilingual Speech Evaluation: Case Studies on English, Malay and Tamil](https://mlsanity.com/paper/2107.03675/) (2021)
- [An Approach to Mispronunciation Detection and Diagnosis with Acoustic, Phonetic and Linguistic (APL) Embeddings](https://mlsanity.com/paper/2110.07274/) (2021)
- [Automatic Pronunciation Assessment -- A Review](https://mlsanity.com/paper/2310.13974/) (2023)
- [Towards Efficient and Multifaceted Computer-assisted Pronunciation Training Leveraging Hierarchical Selective State Space Model and Decoupled Cross-entropy Loss](https://mlsanity.com/paper/2502.07575/) (2025)
- [A transfer learning based approach for pronunciation scoring](https://mlsanity.com/paper/2111.00976/) (2021)
- [Score-balanced Loss for Multi-aspect Pronunciation Assessment](https://mlsanity.com/paper/2305.16664/) (2023)
- [End-to-End Word-Level Pronunciation Assessment with MASK Pre-training](https://mlsanity.com/paper/2306.02682/) (2023)
- [Improving Non-native Word-level Pronunciation Scoring with Phone-level Mixup Data Augmentation and Multi-source Information](https://mlsanity.com/paper/2203.01826/) (2022)
- [L1-aware Multilingual Mispronunciation Detection Framework](https://mlsanity.com/paper/2309.07719/) (2023)
- [Preserving Phonemic Distinctions for Ordinal Regression: A Novel Loss Function for Automatic Pronunciation Assessment](https://mlsanity.com/paper/2310.01839/) (2023)
- [Acoustic Feature Mixup for Balanced Multi-aspect Pronunciation Assessment](https://mlsanity.com/paper/2406.15723/) (2024)
- [SpeechBlender: Speech Augmentation Framework for Mispronunciation Data Generation](https://mlsanity.com/paper/2211.00923/) (2022)
- [Enhancing GOP in CTC-Based Mispronunciation Detection with Phonological Knowledge](https://mlsanity.com/paper/2506.02080/) (2025)
- [Self-supervised Speech Comparison for L2 Phone, Rhythm, and Intonation Scoring](https://mlsanity.com/paper/2607.13721/) (2026)
- [Evaluating Logit-Based GOP Scores for Mispronunciation Detection](https://mlsanity.com/paper/2506.12067/) (2025)
- [Segmentation-free Goodness of Pronunciation](https://mlsanity.com/paper/2507.16838/) (2025)
- [Goodness of Pronunciation Pipelines for OOV Problem](https://mlsanity.com/paper/2209.03787/) (2022)
- [Mispronunciation detection using self-supervised speech representations](https://mlsanity.com/paper/2307.16324/) (2023)
- [Semi-supervised Learning For Robust Speech Evaluation](https://mlsanity.com/paper/2409.14666/) (2024)
- [JCAPT: A Joint Modeling Approach for CAPT](https://mlsanity.com/paper/2506.19315/) (2025)
- [MuFFIN: Multifaceted Pronunciation Feedback Model with Interactive Hierarchical Neural Modeling](https://mlsanity.com/paper/2510.04956/) (2025)
- [Multi-granularity Interactive Attention Framework for Residual Hierarchical Pronunciation Assessment](https://mlsanity.com/paper/2601.01745/) (2026)

## Abstract (arXiv)

Mispronunciation detection is an essential component of the Computer-Assisted Pronunciation Training (CAPT) systems. State-of-the-art mispronunciation detection models use Deep Neural Networks (DNN) for acoustic modeling, and a Goodness of Pronunciation (GOP) based algorithm for pronunciation scoring. However, GOP based scoring models have two major limitations: i.e., (i) They depend on forced alignment which splits the speech into phonetic segments and independently use them for scoring, which neglects the transitions between phonemes within the segment; (ii) They only focus on phonetic segments, which fails to consider the context effects across phonemes (such as liaison, omission, incomplete plosive sound, etc.). In this work, we propose the Context-aware Goodness of Pronunciation (CaGOP) scoring model. Particularly, two factors namely the transition factor and the duration factor are injected into CaGOP scoring. The transition factor identifies the transitions between phonemes and applies them to weight the frame-wise GOP. Moreover, a self-attention based phonetic duration modeling is proposed to introduce the duration factor into the scoring model. The proposed scoring model significantly outperforms baselines, achieving 20% and 12% relative improvement over the GOP model on the phoneme-level and sentence-level mispronunciation detection respectively.