# L2 proficiency assessment using self-supervised speech representations

arXiv: https://arxiv.org/abs/2211.08849 · HTML: https://mlsanity.com/paper/2211.08849/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 33 references that are in the map.
- First posted on arXiv 16 Nov 2022.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [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)
- [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)
- [Proficiency assessment of L2 spoken English using wav2vec 2.0](https://mlsanity.com/paper/2210.13168/) (2022)
- [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)

## Built on by (most cited first, 4 of 4)

- [Preserving Phonemic Distinctions for Ordinal Regression: A Novel Loss Function for Automatic Pronunciation Assessment](https://mlsanity.com/paper/2310.01839/) (2023)
- [The NTNU System at the S&I Challenge 2025 SLA Open Track](https://mlsanity.com/paper/2506.05121/) (2025)
- [Automatic Proficiency Assessment in L2 English Learners](https://mlsanity.com/paper/2505.02615/) (2025)
- [Mitigating Data Imbalance in Automated Speaking Assessment](https://mlsanity.com/paper/2509.03010/) (2025)

## Abstract (arXiv)

There has been a growing demand for automated spoken language assessment systems in recent years. A standard pipeline for this process is to start with a speech recognition system and derive features, either hand-crafted or based on deep-learning, that exploit the transcription and audio. Though these approaches can yield high performance systems, they require speech recognition systems that can be used for L2 speakers, and preferably tuned to the specific form of test being deployed. Recently a self-supervised speech representation based scheme, requiring no speech recognition, was proposed. This work extends the initial analysis conducted on this approach to a large scale proficiency test, Linguaskill, that comprises multiple parts, each designed to assess different attributes of a candidate's speaking proficiency. The performance of the self-supervised, wav2vec 2.0, system is compared to a high performance hand-crafted assessment system and a BERT-based text system both of which use speech transcriptions. Though the wav2vec 2.0 based system is found to be sensitive to the nature of the response, it can be configured to yield comparable performance to systems requiring a speech transcription, and yields gains when appropriately combined with standard approaches.