# DeRAGEC: Denoising Named Entity Candidates with Synthetic Rationale for ASR Error Correction Source: [DeRAGEC: Denoising Named Entity Candidates with Synthetic Rationale for ASR Error Correction]() Collection index: [llms.txt]() Data updated: 2026-01-04T04:24:55.062Z Authors: Solee Im, Wonjun Lee, Jinmyeong An, Yunsu Kim, Jungseul Ok, Gary Geunbae Lee Published: 2025-06-09T07:37:50.000Z ## 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: https://github.com/solee0022/deragec ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [ASR Error Correction on CommonVoice (test)](): ASR Error Correction; CommonVoice (CV) (test); WER: 6. - [ASR Error Correction on STOP (test)](): ASR Error Correction; STOP (test); WER: 5.8. ## Original sources and implementation [Original paper]() [PDF]() [Implementation]()