Introduces error-aware TF-IDF RAG that raises error-aware hit rate from 53.7% to 90.9% and lowers WER from 23.06% to 18.83% on Persian FLEURS data.
Gec-rag: Improving generative error correction via retrieval-augmented generation for automatic speech recognition systems
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A survey that classifies non-intrusive ASR refinement methods into five categories, reviews domain adaptation and evaluation datasets, proposes standardized metrics, and identifies future research directions.
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Error-Aware TF-IDF Retrieval-Augmented Generation for ASR Error Correction
Introduces error-aware TF-IDF RAG that raises error-aware hit rate from 53.7% to 90.9% and lowers WER from 23.06% to 18.83% on Persian FLEURS data.
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Non-Intrusive Automatic Speech Recognition Refinement: A Survey
A survey that classifies non-intrusive ASR refinement methods into five categories, reviews domain adaptation and evaluation datasets, proposes standardized metrics, and identifies future research directions.