Work

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

Year: 2025

Type: conference-paper

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.Our source code is publicly available at

DOI: 10.18653/v1/2025.findings-acl.786

Authors: Solee Im, Wonjun Lee, JinMyeong An, Yunsu Kim, Jungseul Ok, Gary Lee

Institutions: Pohang University of Science and Technology, Samsung (South Korea), Samsung Electronics (South Korea)

Language: English

Topic: Speech Recognition and Synthesis

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold