D e RAGEC : Denoising Named Entity Candidates with Synthetic Rationale for ASR Error Correction (2025)

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.

## introduction recent studies @xcite @xcite have demonstrated that postprocessing speech recognition transcriptions with large language models (llms) can significantly enhance the accuracy of automatic speech recognition (asr). in particular, generative error correction (gec) has been proposed to refine asr outputs @xcite . moreover, recent work has advanced gec into a multi-modal paradigm @xcite ) that jointly conditions on both the textual transcript and the corresponding audio to further improve correction quality. under the gec framework, an asr model first processes the user's speech and outputs multiple hypotheses @xcite through beam search decoding. an llm then refines these hypotheses, either by reranking words across different hypothesis combinations or by predicting contextually appropriate * equally contributed replacements. this approach leverages the extensive linguistic knowledge and powerful generation capabilities of pre-trained llms, which are trained on large-scale text corpora @xcite . despite its effectiveness in improving asr accuracy, gec faces a critical limitation: its inability to reliably introduce words absent from the initial hypotheses, particularly named entities (nes) @xcite . this challenge stems from inherent biases in llms, which, due to their exposure to large, diverse corpora, tend to favor high-frequency words and expressions @xcite . as a result, rare or out-of-vocabulary nes-especially those missing from the asr hypotheses-are difficult to recover with high accuracy @xcite . in response, retrievalaugmented methods @xcite have recently gained traction as a way to incorporate external knowledge for more accurate ne correction. a notable example is retrieval-augmented generative error correction (ragec), which augments gec by retrieving relevant nes from an external knowledge base and integrating them into the llm's input context @xcite . nonetheless, the presence of irrelevant or weakly related nes often introduces noise, thereby undermining correction performance @xcite . current solutions typically handle this noise implicitly, relying heavily on the llm's internal gec capabilities. in this work, we propose an explicit method for denoising ne candidates, which filters out irrelevant nes before the gec process. our approach combines phonetic score, augmented ne definitions, and synthetic rationales within an in-context learning (icl) framework. by directly reducing noise in the retrieved ne candidates-without the need for additional training-we minimize the system's reliance on both asr outputs and llm inference. we tested our method on the commonvoice and stop speech datasets, achieving a 28% relative reduction in word error rate (wer). ## conclusion deragec is a novel, training-free approach that enhances named entity (ne) correction in asr systems by filtering noisy ne candidates using phonetic similarity, augmented definitions, and synthetic rationales. experiments on commonvoice and stop datasets show that deragec outperforms baseline asr and ragec models, achieving a 28% relative reduction in wer and improved ne hit ratios. these results highlight deragec's effectiveness in ne correction, even compared to an oracle setting, with potential for broader asr applications.