# Automatic Pronunciation Assessment using Self-Supervised Speech Representation Learning

arXiv: https://arxiv.org/abs/2204.03863 · HTML: https://mlsanity.com/paper/2204.03863/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 35 references that are in the map.
- First posted on arXiv 8 Apr 2022.

## Builds on (most cited first, 15 of 15)

- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [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)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [SUPERB: Speech processing Universal PERformance Benchmark](https://mlsanity.com/paper/2105.01051/) (2021)
- [Layer-wise Analysis of a Self-supervised Speech Representation Model](https://mlsanity.com/paper/2107.04734/) (2021)
- [Mockingjay: Unsupervised Speech Representation Learning with Deep Bidirectional Transformer Encoders](https://mlsanity.com/paper/1910.12638/) (2019)
- [TERA: Self-Supervised Learning of Transformer Encoder Representation for Speech](https://mlsanity.com/paper/2007.06028/) (2020)
- [Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Training](https://mlsanity.com/paper/2104.01027/) (2021)
- [speechocean762: An Open-Source Non-native English Speech Corpus For Pronunciation Assessment](https://mlsanity.com/paper/2104.01378/) (2021)
- [What all do audio transformer models hear? Probing Acoustic Representations for Language Delivery and its Structure](https://mlsanity.com/paper/2101.00387/) (2021)
- [Probing Acoustic Representations for Phonetic Properties](https://mlsanity.com/paper/2010.13007/) (2020)
- [An End-to-End Mispronunciation Detection System for L2 English Speech Leveraging Novel Anti-Phone Modeling](https://mlsanity.com/paper/2005.11950/) (2020)
- [An Effective End-to-End Modeling Approach for Mispronunciation Detection](https://mlsanity.com/paper/2005.08440/) (2020)
- [Improving End-To-End Modeling for Mispronunciation Detection with Effective Augmentation Mechanisms](https://mlsanity.com/paper/2110.08731/) (2021)

## Built on by (most cited first, 29 of 29)

- [What Do Self-Supervised Speech Models Know About Words?](https://mlsanity.com/paper/2307.00162/) (2023)
- [Proficiency assessment of L2 spoken English using wav2vec 2.0](https://mlsanity.com/paper/2210.13168/) (2022)
- [N-Shot Benchmarking of Whisper on Diverse Arabic Speech Recognition](https://mlsanity.com/paper/2306.02902/) (2023)
- [Assessment of L2 Oral Proficiency using Speech Large Language Models](https://mlsanity.com/paper/2505.21148/) (2025)
- [MultiPA: A Multi-task Speech Pronunciation Assessment Model for Open Response Scenarios](https://mlsanity.com/paper/2308.12490/) (2023)
- [Automatic Pronunciation Assessment -- A Review](https://mlsanity.com/paper/2310.13974/) (2023)
- [An Effective Automated Speaking Assessment Approach to Mitigating Data Scarcity and Imbalanced Distribution](https://mlsanity.com/paper/2404.07575/) (2024)
- [LLMs in Education: Novel Perspectives, Challenges, and Opportunities](https://mlsanity.com/paper/2409.11917/) (2024)
- [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)
- [Opportunities and Challenges of LLMs in Education: An NLP Perspective](https://mlsanity.com/paper/2507.22753/) (2025)
- [Zero-Shot Automatic Pronunciation Assessment](https://mlsanity.com/paper/2305.19563/) (2023)
- [L2 proficiency assessment using self-supervised speech representations](https://mlsanity.com/paper/2211.08849/) (2022)
- [End-to-End Word-Level Pronunciation Assessment with MASK Pre-training](https://mlsanity.com/paper/2306.02682/) (2023)
- [Pronunciation Assessment with Multi-modal Large Language Models](https://mlsanity.com/paper/2407.09209/) (2024)
- [Phonetic and Prosody-aware Self-supervised Learning Approach for Non-native Fluency Scoring](https://mlsanity.com/paper/2305.11438/) (2023)
- [Addressing Cold Start Problem for End-to-end Automatic Speech Scoring](https://mlsanity.com/paper/2306.14310/) (2023)
- [The NTNU System at the S&I Challenge 2025 SLA Open Track](https://mlsanity.com/paper/2506.05121/) (2025)
- [The complementary roles of non-verbal cues for Robust Pronunciation Assessment](https://mlsanity.com/paper/2309.07739/) (2023)
- [Optimizing Automatic Speech Assessment: W-RankSim Regularization and Hybrid Feature Fusion Strategies](https://mlsanity.com/paper/2406.10873/) (2024)
- [Segmentation-free Goodness of Pronunciation](https://mlsanity.com/paper/2507.16838/) (2025)
- [A Finetuned SpeechLLM for Joint Multi-Granular L2 Assessment and Natural-Language Rationales](https://mlsanity.com/paper/2606.09470/) (2026)
- [An End-to-End Approach for Child Reading Assessment in the Xhosa Language](https://mlsanity.com/paper/2505.17371/) (2025)
- [CBF-AFA: Chunk-Based Multi-SSL Fusion for Automatic Fluency Assessment](https://mlsanity.com/paper/2506.20243/) (2025)
- [Beyond Modality Limitations: A Unified MLLM Approach to Automated Speaking Assessment with Effective Curriculum Learning](https://mlsanity.com/paper/2508.12591/) (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)
- [Goodness-of-pronunciation without phoneme time alignment](https://mlsanity.com/paper/2603.25150/) (2026)
- [Light-weight Pronunciation Assessment via Discrete Speech Token Surprisal](https://mlsanity.com/paper/2606.19910/) (2026)
- [When Speech Meets Lips: Interpretable Audio-Visual Synchronization for L2 Pronunciation Assessment](https://mlsanity.com/paper/2609.06788/) (2026)

## Abstract (arXiv)

Self-supervised learning (SSL) approaches such as wav2vec 2.0 and HuBERT models have shown promising results in various downstream tasks in the speech community. In particular, speech representations learned by SSL models have been shown to be effective for encoding various speech-related characteristics. In this context, we propose a novel automatic pronunciation assessment method based on SSL models. First, the proposed method fine-tunes the pre-trained SSL models with connectionist temporal classification to adapt the English pronunciation of English-as-a-second-language (ESL) learners in a data environment. Then, the layer-wise contextual representations are extracted from all across the transformer layers of the SSL models. Finally, the automatic pronunciation score is estimated using bidirectional long short-term memory with the layer-wise contextual representations and the corresponding text. We show that the proposed SSL model-based methods outperform the baselines, in terms of the Pearson correlation coefficient, on datasets of Korean ESL learner children and Speechocean762. Furthermore, we analyze how different representations of transformer layers in the SSL model affect the performance of the pronunciation assessment task.