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Error-Aware TF-IDF RAG for ASR Error Correction
A lightweight RAG approach uses phonetically-aware TF-IDF retrieval to correct ASR hallucinations of rare entities and domain-specific terms.
A new paper on arXiv tackles the persistent problem of hallucinated rare entities and domain-specific terms in end-to-end automatic speech recognition (ASR) systems. This issue is especially acute in low-resource languages where training data is limited.
The proposed method introduces an error-aware TF-IDF retrieval-augmented generation framework that recognizes phonetic misrecognitions common in ASR output without relying on heavyweight cross-modal embeddings. This balances retrieval precision with computational efficiency, making it practical for real-world deployment.
Published on arXiv cs.CL on June 25, 2026, this work offers a practical path for improving ASR accuracy in specialized domains and low-resource language settings where conventional approaches fall short.
Why it matters
Delivers a lightweight and efficient RAG approach for ASR correction in low-resource and domain-specific settings.