# GEC-RAG: Improving Generative Error Correction via Retrieval-Augmented Generation for Automatic Speech Recognition Systems

arXiv: https://arxiv.org/abs/2501.10734 · HTML: https://mlsanity.com/paper/2501.10734/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Conversational and speech RAG, one of 15 directions in Retrieval-augmented generation.
- Builds on 15 of its 23 references that are in the map.
- First posted on arXiv 18 Jan 2025.

## Placed in

- Retrieval-augmented generation / [Conversational and speech RAG](https://mlsanity.com/rag/conversational-and-speech-rag/)

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

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [End-to-End Speech Recognition: A Survey](https://mlsanity.com/paper/2303.03329/) (2023)
- [A Density Ratio Approach to Language Model Fusion in End-To-End Automatic Speech Recognition](https://mlsanity.com/paper/2002.11268/) (2020)
- [A Comparison of Techniques for Language Model Integration in Encoder-Decoder Speech Recognition](https://mlsanity.com/paper/1807.10857/) (2018)
- [A spelling correction model for end-to-end speech recognition](https://mlsanity.com/paper/1902.07178/) (2019)
- [Can Generative Large Language Models Perform ASR Error Correction?](https://mlsanity.com/paper/2307.04172/) (2023)
- [Correction of Automatic Speech Recognition with Transformer Sequence-to-sequence Model](https://mlsanity.com/paper/1910.10697/) (2019)
- [N-best T5: Robust ASR Error Correction using Multiple Input Hypotheses and Constrained Decoding Space](https://mlsanity.com/paper/2303.00456/) (2023)
- [ASR Error Correction using Large Language Models](https://mlsanity.com/paper/2409.09554/) (2024)
- [Error Correction in ASR using Sequence-to-Sequence Models](https://mlsanity.com/paper/2202.01157/) (2022)
- [Listen Again and Choose the Right Answer: A New Paradigm for Automatic Speech Recognition with Large Language Models](https://mlsanity.com/paper/2405.10025/) (2024)
- [Retrieval Augmented Correction of Named Entity Speech Recognition Errors](https://mlsanity.com/paper/2409.06062/) (2024)
- [Failing Forward: Improving Generative Error Correction for ASR with Synthetic Data and Retrieval Augmentation](https://mlsanity.com/paper/2410.13198/) (2024)
- [LA-RAG:Enhancing LLM-based ASR Accuracy with Retrieval-Augmented Generation](https://mlsanity.com/paper/2409.08597/) (2024)

## Built on by (most cited first, 5 of 5)

- [MedSpeak: A Knowledge Graph-Aided ASR Error Correction Framework for Spoken Medical QA](https://mlsanity.com/paper/2602.00981/) (2026)
- [Seewo's Submission to MLC-SLM: Lessons learned from Speech Reasoning Language Models](https://mlsanity.com/paper/2506.13300/) (2025)
- [Game-Oriented ASR Error Correction via RAG-Enhanced LLM](https://mlsanity.com/paper/2509.23630/) (2025)
- [Incorporating Error Level Noise Embedding for Improving LLM-Assisted Robustness in Persian Speech Recognition](https://mlsanity.com/paper/2512.17247/) (2025)
- [Error-Aware TF-IDF Retrieval-Augmented Generation for ASR Error Correction](https://mlsanity.com/paper/2606.24915/) (2026)

## Abstract (arXiv)

Automatic Speech Recognition (ASR) systems have demonstrated remarkable performance across various applications. However, limited data and the unique language features of specific domains, such as low-resource languages, significantly degrade their performance and lead to higher Word Error Rates (WER). In this study, we propose Generative Error Correction via Retrieval-Augmented Generation (GEC-RAG), a novel approach designed to improve ASR accuracy for low-resource domains, like Persian. Our approach treats the ASR system as a black-box, a common practice in cloud-based services, and proposes a Retrieval-Augmented Generation (RAG) approach within the In-Context Learning (ICL) scheme to enhance the quality of ASR predictions. By constructing a knowledge base that pairs ASR predictions (1-best and 5-best hypotheses) with their corresponding ground truths, GEC-RAG retrieves lexically similar examples to the ASR transcription using the Term Frequency-Inverse Document Frequency (TF-IDF) measure. This process provides relevant error patterns of the system alongside the ASR transcription to the Generative Large Language Model (LLM), enabling targeted corrections. Our results demonstrate that this strategy significantly reduces WER in Persian and highlights a potential for domain adaptation and low-resource scenarios. This research underscores the effectiveness of using RAG in enhancing ASR systems without requiring direct model modification or fine-tuning, making it adaptable to any domain by simply updating the transcription knowledge base with domain-specific data.