# Proficiency assessment of L2 spoken English using wav2vec 2.0

arXiv: https://arxiv.org/abs/2210.13168 · HTML: https://mlsanity.com/paper/2210.13168/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 43 references that are in the map.
- First posted on arXiv 24 Oct 2022.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [SUPERB: Speech processing Universal PERformance Benchmark](https://mlsanity.com/paper/2105.01051/) (2021)
- [Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Training](https://mlsanity.com/paper/2104.01027/) (2021)
- [What all do audio transformer models hear? Probing Acoustic Representations for Language Delivery and its Structure](https://mlsanity.com/paper/2101.00387/) (2021)
- [Automatic Pronunciation Assessment using Self-Supervised Speech Representation Learning](https://mlsanity.com/paper/2204.03863/) (2022)
- [TLT-school: a Corpus of Non Native Children Speech](https://mlsanity.com/paper/2001.08051/) (2020)
- [ASR-Free Pronunciation Assessment](https://mlsanity.com/paper/2005.11902/) (2020)
- [Dolphin: A Spoken Language Proficiency Assessment System for Elementary Education](https://mlsanity.com/paper/1908.00358/) (2019)
- [Automatic assessment of spoken language proficiency of non-native children](https://mlsanity.com/paper/1903.06409/) (2019)

## Built on by (most cited first, 20 of 20)

- [What Do Self-Supervised Speech Models Know About Words?](https://mlsanity.com/paper/2307.00162/) (2023)
- [Assessment of L2 Oral Proficiency using Speech Large Language Models](https://mlsanity.com/paper/2505.21148/) (2025)
- [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)
- [Natural Language-based Assessment of L2 Oral Proficiency using LLMs](https://mlsanity.com/paper/2507.10200/) (2025)
- [L2 proficiency assessment using self-supervised speech representations](https://mlsanity.com/paper/2211.08849/) (2022)
- [The NTNU System at the S&I Challenge 2025 SLA Open Track](https://mlsanity.com/paper/2506.05121/) (2025)
- [One Whisper to Grade Them All](https://mlsanity.com/paper/2507.17918/) (2025)
- [Optimizing Automatic Speech Assessment: W-RankSim Regularization and Hybrid Feature Fusion Strategies](https://mlsanity.com/paper/2406.10873/) (2024)
- [Automatic Proficiency Assessment in L2 English Learners](https://mlsanity.com/paper/2505.02615/) (2025)
- [Emphasis Sensitivity in Speech Representations](https://mlsanity.com/paper/2508.11566/) (2025)
- [Beyond Modality Limitations: A Unified MLLM Approach to Automated Speaking Assessment with Effective Curriculum Learning](https://mlsanity.com/paper/2508.12591/) (2025)
- [Mitigating Data Imbalance in Automated Speaking Assessment](https://mlsanity.com/paper/2509.03010/) (2025)
- [An Effective Strategy for Modeling Score Ordinality and Non-uniform Intervals in Automated Speaking Assessment](https://mlsanity.com/paper/2509.03372/) (2025)
- [MuFFIN: Multifaceted Pronunciation Feedback Model with Interactive Hierarchical Neural Modeling](https://mlsanity.com/paper/2510.04956/) (2025)
- [Goodness-of-pronunciation without phoneme time alignment](https://mlsanity.com/paper/2603.25150/) (2026)
- [Controlling Implicit Shortcut Reliance in L2 Spoken English Auto-markers](https://mlsanity.com/paper/2607.16085/) (2026)
- [Bias Analysis of L2 Speaking Assessment Systems Using Concept Activation Vectors](https://mlsanity.com/paper/2608.06300/) (2026)
- [Interpretable, Fairly Evaluated Automated L2 Speaking Assessment that Beats the Single-Human Ceiling and Why Pause Encoding Does Not Change LLM Fluency Scores](https://mlsanity.com/paper/2608.26137/) (2026)

## Abstract (arXiv)

The increasing demand for learning English as a second language has led to a growing interest in methods for automatically assessing spoken language proficiency. Most approaches use hand-crafted features, but their efficacy relies on their particular underlying assumptions and they risk discarding potentially salient information about proficiency. Other approaches rely on transcriptions produced by ASR systems which may not provide a faithful rendition of a learner's utterance in specific scenarios (e.g., non-native children's spontaneous speech). Furthermore, transcriptions do not yield any information about relevant aspects such as intonation, rhythm or prosody. In this paper, we investigate the use of wav2vec 2.0 for assessing overall and individual aspects of proficiency on two small datasets, one of which is publicly available. We find that this approach significantly outperforms the BERT-based baseline system trained on ASR and manual transcriptions used for comparison.