mlsanityData to 7 Oct 2026

DeRAGEC: Denoising Named Entity Candidates with Synthetic Rationale for ASR Error Correction

arXiv 2506.07510 ↗First posted 9 Jun 2025cs.CLcited by 4

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Abstract (arXiv)

We present DeRAGEC, a method for improving Named Entity (NE) correction in Automatic Speech Recognition (ASR) systems. By extending the Retrieval-Augmented Generative Error Correction (RAGEC) framework, DeRAGEC employs synthetic denoising rationales to filter out noisy NE candidates before correction. By leveraging phonetic similarity and augmented definitions, it refines noisy retrieved NEs using in-context learning, requiring no additional training. Experimental results on CommonVoice and STOP datasets show significant improvements in Word Error Rate (WER) and NE hit ratio, outperforming baseline ASR and RAGEC methods. Specifically, we achieved a 28% relative reduction in WER compared to ASR without postprocessing. Our source code is publicly available at: https://github.com/solee0022/deragec

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 18 of its 30 references that are in the map. First posted on arXiv 9 Jun 2025.

Builds on

18 of 30 references.

References · 18

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2024The Llama 3 Herd of Modelscited by 14,192
  3. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  4. 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 3,972
  5. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,126
  6. 2019Common Voice: A Massively-Multilingual Speech Corpuscited by 1,358
  7. 2022Atlas: Few-shot Learning with Retrieval Augmented Language Modelscited by 940
  8. 2023Bias and Fairness in Large Language Models: A Surveycited by 626
  9. 2023Making Retrieval-Augmented Language Models Robust to Irrelevant Contextcited by 364
  10. 2024The Power of Noise: Redefining Retrieval for RAG Systemscited by 275
  11. 2022Large Language Models with Controllable Working Memorycited by 169
  12. 2023Generative Speech Recognition Error Correction with Large Language Models and Task-Activating Promptingcited by 71
6 more
  1. 2023Can Generative Large Language Models Perform ASR Error Correction?cited by 56
  2. 2022STOP: A dataset for Spoken Task Oriented Semantic Parsingcited by 22
  3. 2024Retrieval Augmented Correction of Named Entity Speech Recognition Errorscited by 10
  4. 2024Contextualization of ASR with LLM using phonetic retrieval-based augmentationcited by 10
  5. 2024Failing Forward: Improving Generative Error Correction for ASR with Synthetic Data and Retrieval Augmentationcited by 9
  6. 2022Space-Efficient Representation of Entity-centric Query Language Modelscited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026Ontology Memory-Augmented ASR Correction for Long Text-Speech Interleaved Conversationscited by 1
  2. 2026Retrieval-Augmented Self-Taught Reasoning Model with Adaptive Chain-of-Thought for ASR Named Entity Correctioncited by 0
  3. 2026AVERT: Audio-Verified Adjudication for Spoken Dialogue State Trackingcited by 0
  4. 2026Talk2Agent: Benchmarking Voice Interfaces for Text Agentscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context
  2. 2025Improving Speech Recognition of Named Entities in Classroom Speech with LLM Revision and Phonetic-Semantic Context
  3. 2025GEC-RAG: Improving Generative Error Correction via Retrieval-Augmented Generation for Automatic Speech Recognition Systems
  4. 2026RECOVER: Robust Entity Correction via agentic Orchestration of hypothesis Variants for Evidence-based Recovery
  5. 2026Error-Aware TF-IDF Retrieval-Augmented Generation for ASR Error Correction
  6. 2023Retrieve and Copy: Scaling ASR Personalization to Large Catalogs
  7. 2024Large Language Models are Efficient Learners of Noise-Robust Speech Recognition
  8. 2026Better Retrieval, Worse Robustness: How Multi-hop RAG Amplifies Upstream ASR Errors