# Retrieval Augmented Correction of Named Entity Speech Recognition Errors

arXiv: https://arxiv.org/abs/2409.06062 · HTML: https://mlsanity.com/paper/2409.06062/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Sep 2024.

## Built on by (most cited first, 10 of 10)

- [GEC-RAG: Improving Generative Error Correction via Retrieval-Augmented Generation for Automatic Speech Recognition Systems](https://mlsanity.com/paper/2501.10734/) (2025)
- [LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context](https://mlsanity.com/paper/2505.17410/) (2025)
- [DeRAGEC: Denoising Named Entity Candidates with Synthetic Rationale for ASR Error Correction](https://mlsanity.com/paper/2506.07510/) (2025)
- [Optimizing Estonian TV Subtitles with Semi-supervised Learning and LLMs](https://mlsanity.com/paper/2501.05234/) (2025)
- [MedSpeak: A Knowledge Graph-Aided ASR Error Correction Framework for Spoken Medical QA](https://mlsanity.com/paper/2602.00981/) (2026)
- [Auto Review: Second Stage Error Detection for Highly Accurate Information Extraction from Phone Conversations](https://mlsanity.com/paper/2506.05400/) (2025)
- [Game-Oriented ASR Error Correction via RAG-Enhanced LLM](https://mlsanity.com/paper/2509.23630/) (2025)
- [Knowledge-Augmented Question Error Correction for Chinese Question Answer System with QuestionRAG](https://mlsanity.com/paper/2511.03410/) (2025)
- [Error-Aware TF-IDF Retrieval-Augmented Generation for ASR Error Correction](https://mlsanity.com/paper/2606.24915/) (2026)
- [Beyond WER: Entity and Disfluency Recall in Accented Conversational ASR](https://mlsanity.com/paper/2609.20828/) (2026)

## Abstract (arXiv)

In recent years, end-to-end automatic speech recognition (ASR) systems have proven themselves remarkably accurate and performant, but these systems still have a significant error rate for entity names which appear infrequently in their training data. In parallel to the rise of end-to-end ASR systems, large language models (LLMs) have proven to be a versatile tool for various natural language processing (NLP) tasks. In NLP tasks where a database of relevant knowledge is available, retrieval augmented generation (RAG) has achieved impressive results when used with LLMs. In this work, we propose a RAG-like technique for correcting speech recognition entity name errors. Our approach uses a vector database to index a set of relevant entities. At runtime, database queries are generated from possibly errorful textual ASR hypotheses, and the entities retrieved using these queries are fed, along with the ASR hypotheses, to an LLM which has been adapted to correct ASR errors. Overall, our best system achieves 33%-39% relative word error rate reductions on synthetic test sets focused on voice assistant queries of rare music entities without regressing on the STOP test set, a publicly available voice assistant test set covering many domains.