DeRAGEC: Denoising Named Entity Candidates with Synthetic Rationale for ASR Error Correction
Solee Im, Wonjun Lee, JinMyeong An, Yunsu Kim, Jungseul Ok, Gary Lee
Abstract
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.
BibTeX
@inproceedings{im-etal-2025-deragec,
title = "{D}e{RAGEC}: Denoising Named Entity Candidates with Synthetic Rationale for {ASR} Error Correction",
author = "Im, Solee and
Lee, Wonjun and
An, JinMyeong and
Kim, Yunsu and
Ok, Jungseul and
Lee, Gary",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.786/",
doi = "10.18653/v1/2025.findings-acl.786",
pages = "15181--15193",
ISBN = "979-8-89176-256-5"
}