# Chain of Correction for Full-text Speech Recognition with Large Language Models

arXiv: https://arxiv.org/abs/2504.01519 · HTML: https://mlsanity.com/paper/2504.01519/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 18 references that are in the map.
- First posted on arXiv 2 Apr 2025.

## Builds on (most cited first, 13 of 13)

- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [A spelling correction model for end-to-end speech recognition](https://mlsanity.com/paper/1902.07178/) (2019)
- [Deliberation Model Based Two-Pass End-to-End Speech Recognition](https://mlsanity.com/paper/2003.07962/) (2020)
- [N-best T5: Robust ASR Error Correction using Multiple Input Hypotheses and Constrained Decoding Space](https://mlsanity.com/paper/2303.00456/) (2023)
- [HyPoradise: An Open Baseline for Generative Speech Recognition with Large Language Models](https://mlsanity.com/paper/2309.15701/) (2023)
- [FastCorrect 2: Fast Error Correction on Multiple Candidates for Automatic Speech Recognition](https://mlsanity.com/paper/2109.14420/) (2021)
- [Multi-task Language Modeling for Improving Speech Recognition of Rare Words](https://mlsanity.com/paper/2011.11715/) (2020)
- [Improving Deliberation by Text-Only and Semi-Supervised Training](https://mlsanity.com/paper/2206.14716/) (2022)
- [Generative error correction for code-switching speech recognition using large language models](https://mlsanity.com/paper/2310.13013/) (2023)
- [Scaling Up Deliberation for Multilingual ASR](https://mlsanity.com/paper/2210.05785/) (2022)
- [Full-text Error Correction for Chinese Speech Recognition with Large Language Model](https://mlsanity.com/paper/2409.07790/) (2024)
- [Large Language Model Should Understand Pinyin for Chinese ASR Error Correction](https://mlsanity.com/paper/2409.13262/) (2024)

## Built on by (most cited first, 1 of 1)

- [Select or Rewrite: Oracle-Guided Fine-Tuning for Generative ASR Post-Editing from N-Best Hypotheses](https://mlsanity.com/paper/2610.15056/) (2026)

## Abstract (arXiv)

Full-text error correction with Large Language Models (LLMs) for Automatic Speech Recognition (ASR) is attracting increased attention for its ability to address a wide range of error types, such as punctuation restoration and inverse text normalization, across long context. However, challenges remain regarding stability, controllability, completeness, and fluency. To mitigate these issues, this paper proposes the Chain of Correction (CoC), which uses a multi-turn chat format to correct errors segment by segment, guided by pre-recognized text and full-text context for better semantic understanding. Utilizing the open-sourced ChFT dataset, we fine-tune a pre-trained LLM to evaluate CoC's performance. Experiments show that CoC significantly outperforms baseline and benchmark systems in correcting full-text ASR outputs. We also analyze correction thresholds to balance under-correction and over-rephrasing, extrapolate CoC on extra-long ASR outputs, and explore using other types of information to guide error correction.