# Towards End-to-End Spoken Grammatical Error Correction

arXiv: https://arxiv.org/abs/2311.05550 · HTML: https://mlsanity.com/paper/2311.05550/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 28 references that are in the map.
- First posted on arXiv 9 Nov 2023.

## Builds on (most cited first, 7 of 7)

- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [GECToR -- Grammatical Error Correction: Tag, Not Rewrite](https://mlsanity.com/paper/2005.12592/) (2020)
- [Grammatical Error Correction: A Survey of the State of the Art](https://mlsanity.com/paper/2211.05166/) (2022)
- [Disfluencies and Human Speech Transcription Errors](https://mlsanity.com/paper/1904.04398/) (2019)
- [Adapting an ASR Foundation Model for Spoken Language Assessment](https://mlsanity.com/paper/2307.09378/) (2023)

## Built on by (most cited first, 6 of 6)

- [Paralinguistics-Aware Speech-Empowered Large Language Models for Natural Conversation](https://mlsanity.com/paper/2402.05706/) (2024)
- [Scaling and Prompting for Improved End-to-End Spoken Grammatical Error Correction](https://mlsanity.com/paper/2505.21137/) (2025)
- [Acoustically Precise Hesitation Tagging Is Essential for End-to-End Verbatim Transcription Systems](https://mlsanity.com/paper/2506.04076/) (2025)
- [End-to-End Spoken Grammatical Error Correction](https://mlsanity.com/paper/2506.18532/) (2025)
- [Data Augmentation for Spoken Grammatical Error Correction](https://mlsanity.com/paper/2507.19374/) (2025)
- [Spoken Grammar Assessment Using LLM](https://mlsanity.com/paper/2410.01579/) (2024)

## Abstract (arXiv)

Grammatical feedback is crucial for L2 learners, teachers, and testers. Spoken grammatical error correction (GEC) aims to supply feedback to L2 learners on their use of grammar when speaking. This process usually relies on a cascaded pipeline comprising an ASR system, disfluency removal, and GEC, with the associated concern of propagating errors between these individual modules. In this paper, we introduce an alternative "end-to-end" approach to spoken GEC, exploiting a speech recognition foundation model, Whisper. This foundation model can be used to replace the whole framework or part of it, e.g., ASR and disfluency removal. These end-to-end approaches are compared to more standard cascaded approaches on the data obtained from a free-speaking spoken language assessment test, Linguaskill. Results demonstrate that end-to-end spoken GEC is possible within this architecture, but the lack of available data limits current performance compared to a system using large quantities of text-based GEC data. Conversely, end-to-end disfluency detection and removal, which is easier for the attention-based Whisper to learn, does outperform cascaded approaches. Additionally, the paper discusses the challenges of providing feedback to candidates when using end-to-end systems for spoken GEC.