DeRAGEC: Denoising Named Entity Candidates with Synthetic Rationale for ASR Error Correction
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866913885141860352 |
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| author | Im, Solee Lee, Wonjun An, Jinmyeong Kim, Yunsu Ok, Jungseul Lee, Gary Geunbae |
| author_facet | Im, Solee Lee, Wonjun An, Jinmyeong Kim, Yunsu Ok, Jungseul Lee, Gary Geunbae |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_07510 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DeRAGEC: Denoising Named Entity Candidates with Synthetic Rationale for ASR Error Correction Im, Solee Lee, Wonjun An, Jinmyeong Kim, Yunsu Ok, Jungseul Lee, Gary Geunbae Computation and Language 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 |
| title | DeRAGEC: Denoising Named Entity Candidates with Synthetic Rationale for ASR Error Correction |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2506.07510 |