# Scaling and Prompting for Improved End-to-End Spoken Grammatical Error Correction

arXiv: https://arxiv.org/abs/2505.21137 · HTML: https://mlsanity.com/paper/2505.21137/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 30 references that are in the map.
- First posted on arXiv 27 May 2025.

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

- [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)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [Grammatical Error Correction: A Survey of the State of the Art](https://mlsanity.com/paper/2211.05166/) (2022)
- [Enhancing Grammatical Error Correction Systems with Explanations](https://mlsanity.com/paper/2305.15676/) (2023)
- [Learn and Don't Forget: Adding a New Language to ASR Foundation Models](https://mlsanity.com/paper/2407.06800/) (2024)
- [GEE! Grammar Error Explanation with Large Language Models](https://mlsanity.com/paper/2311.09517/) (2023)
- [Speak & Improve Corpus 2025: an L2 English Speech Corpus for Language Assessment and Feedback](https://mlsanity.com/paper/2412.11986/) (2024)
- [Speak & Improve Challenge 2025: Tasks and Baseline Systems](https://mlsanity.com/paper/2412.11985/) (2024)
- [Cross-Lingual Transfer Learning for Speech Translation](https://mlsanity.com/paper/2407.01130/) (2024)
- [Fine-tuning Whisper on Low-Resource Languages for Real-World Applications](https://mlsanity.com/paper/2412.15726/) (2024)
- [Investigating the Emergent Audio Classification Ability of ASR Foundation Models](https://mlsanity.com/paper/2311.09363/) (2023)
- [Controlled Generation with Prompt Insertion for Natural Language Explanations in Grammatical Error Correction](https://mlsanity.com/paper/2309.11439/) (2023)
- [Towards End-to-End Spoken Grammatical Error Correction](https://mlsanity.com/paper/2311.05550/) (2023)
- [Task Arithmetic for Language Expansion in Speech Translation](https://mlsanity.com/paper/2409.11274/) (2024)

## Built on by (most cited first, 2 of 2)

- [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)

## Abstract (arXiv)

Spoken Grammatical Error Correction (SGEC) and Feedback (SGECF) are crucial for second language learners, teachers and test takers. Traditional SGEC systems rely on a cascaded pipeline consisting of an ASR, a module for disfluency detection (DD) and removal and one for GEC. With the rise of end-to-end (E2E) speech foundation models, we investigate their effectiveness in SGEC and feedback generation. This work introduces a pseudo-labelling process to address the challenge of limited labelled data, expanding the training data size from 77 hours to approximately 2500 hours, leading to improved performance. Additionally, we prompt an E2E Whisper-based SGEC model with fluent transcriptions, showing a slight improvement in SGEC performance, with more significant gains in feedback generation. Finally, we assess the impact of increasing model size, revealing that while pseudo-labelled data does not yield performance gain for a larger Whisper model, training with prompts proves beneficial.