# Automatic Pronunciation Assessment -- A Review

arXiv: https://arxiv.org/abs/2310.13974 · HTML: https://mlsanity.com/paper/2310.13974/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 146 references that are in the map.
- First posted on arXiv 21 Oct 2023.

## Builds on (most cited first, 27 of 27)

- [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)
- [Scaling Speech Technology to 1,000+ Languages](https://mlsanity.com/paper/2305.13516/) (2023)
- [Self-Supervised Speech Representation Learning: A Review](https://mlsanity.com/paper/2205.10643/) (2022)
- [Exploring wav2vec 2.0 on speaker verification and language identification](https://mlsanity.com/paper/2012.06185/) (2020)
- [speechocean762: An Open-Source Non-native English Speech Corpus For Pronunciation Assessment](https://mlsanity.com/paper/2104.01378/) (2021)
- [Towards One Model to Rule All: Multilingual Strategy for Dialectal Code-Switching Arabic ASR](https://mlsanity.com/paper/2105.14779/) (2021)
- [Transformer-Based Multi-Aspect Multi-Granularity Non-Native English Speaker Pronunciation Assessment](https://mlsanity.com/paper/2205.03432/) (2022)
- [Automatic Pronunciation Assessment using Self-Supervised Speech Representation Learning](https://mlsanity.com/paper/2204.03863/) (2022)
- [Context-aware Goodness of Pronunciation for Computer-Assisted Pronunciation Training](https://mlsanity.com/paper/2008.08647/) (2020)
- [3M: An Effective Multi-view, Multi-granularity, and Multi-aspect Modeling Approach to English Pronunciation Assessment](https://mlsanity.com/paper/2208.09110/) (2022)
- [A Full Text-Dependent End to End Mispronunciation Detection and Diagnosis with Easy Data Augmentation Techniques](https://mlsanity.com/paper/2104.08428/) (2021)
- [Language-agnostic Multilingual Modeling](https://mlsanity.com/paper/2004.09571/) (2020)
- [Hierarchical Pronunciation Assessment with Multi-Aspect Attention](https://mlsanity.com/paper/2211.08102/) (2022)
- [TLT-school: a Corpus of Non Native Children Speech](https://mlsanity.com/paper/2001.08051/) (2020)
- [Multilingual Speech Evaluation: Case Studies on English, Malay and Tamil](https://mlsanity.com/paper/2107.03675/) (2021)
- [Improving Mispronunciation Detection with Wav2vec2-based Momentum Pseudo-Labeling for Accentedness and Intelligibility Assessment](https://mlsanity.com/paper/2203.15937/) (2022)
- [Detection of Lexical Stress Errors in Non-Native (L2) English with Data Augmentation and Attention](https://mlsanity.com/paper/2012.14788/) (2020)
- [An Approach to Mispronunciation Detection and Diagnosis with Acoustic, Phonetic and Linguistic (APL) Embeddings](https://mlsanity.com/paper/2110.07274/) (2021)
- [Multi-View Multi-Task Representation Learning for Mispronunciation Detection](https://mlsanity.com/paper/2306.01845/) (2023)
- [A transfer learning based approach for pronunciation scoring](https://mlsanity.com/paper/2111.00976/) (2021)
- [Improving Non-native Word-level Pronunciation Scoring with Phone-level Mixup Data Augmentation and Multi-source Information](https://mlsanity.com/paper/2203.01826/) (2022)
- [An End-to-end Approach for Lexical Stress Detection based on Transformer](https://mlsanity.com/paper/1911.04862/) (2019)
- [Experiments of ASR-based mispronunciation detection for children and adult English learners](https://mlsanity.com/paper/2104.05980/) (2021)
- [Unsupervised speech intelligibility assessment with utterance level alignment distance between teacher and learner Wav2Vec-2.0 representations](https://mlsanity.com/paper/2306.08845/) (2023)
- [The complementary roles of non-verbal cues for Robust Pronunciation Assessment](https://mlsanity.com/paper/2309.07739/) (2023)

## Built on by (most cited first, 7 of 7)

- [VoiceBench: Benchmarking LLM-Based Voice Assistants](https://mlsanity.com/paper/2410.17196/) (2024)
- [Non-native Children's Automatic Speech Assessment Challenge (NOCASA)](https://mlsanity.com/paper/2504.20678/) (2025)
- [Automatic Pronunciation Error Detection and Correction of the Holy Quran's Learners Using Deep Learning](https://mlsanity.com/paper/2509.00094/) (2025)
- [Fine-Tuning Large Multimodal Models for Automatic Pronunciation Assessment](https://mlsanity.com/paper/2509.15701/) (2025)
- [A Native-Reference Coordinate Geometry for L2 Pronunciation Deviation Using Self-Supervised Speech Models](https://mlsanity.com/paper/2609.28060/) (2026)
- [Multi-task Pretraining for Enhancing Interpretable L2 Pronunciation Assessment](https://mlsanity.com/paper/2509.16876/) (2025)
- [Automatic Detection and Analysis of Singing Mistakes for Music Pedagogy](https://mlsanity.com/paper/2602.06917/) (2026)

## Abstract (arXiv)

Pronunciation assessment and its application in computer-aided pronunciation training (CAPT) have seen impressive progress in recent years. With the rapid growth in language processing and deep learning over the past few years, there is a need for an updated review. In this paper, we review methods employed in pronunciation assessment for both phonemic and prosodic. We categorize the main challenges observed in prominent research trends, and highlight existing limitations, and available resources. This is followed by a discussion of the remaining challenges and possible directions for future work.