用合成理由过滤噪声实体，提升语音识别纠错效果
DeRAGEC: Denoising Named Entity Candidates with Synthetic Rationale for ASR Error Correction
- 通过合成理由和上下文学习过滤噪声实体候选
- 在CommonVoice和STOP数据集上实现28%相对WER降低
- 无需额外训练，适合语音识别后处理场景
我们提出DeRAGEC，一种改进自动语音识别（ASR）系统中命名实体（NE）纠错的方法。在检索增强生成纠错（RAGEC）框架基础上，DeRAGEC利用合成去噪理由，在纠错前过滤掉噪声实体候选。结合语音相似性和增强定义，通过上下文学习精炼检索到的噪声实体，无需额外训练。在CommonVoice和STOP数据集上的实验表明，该方法显著降低了词错误率（WER）并提升了实体命中率，相较于无后处理的ASR系统，实现了28%的相对WER下降。代码已公开于：https://github.com/solee0022/deragec
原文摘要 · Abstract (English)
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
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