Context-Enhanced Granular Edit Representation for Efficient and Accurate ASR Post-editing

• URL: http://arxiv.org/abs/2509.14263v1
• Date: Sat, 13 Sep 2025 16:57:32 GMT
• Title: Context-Enhanced Granular Edit Representation for Efficient and Accurate ASR Post-editing
• Authors: Luan Vejsiu, Qianyu Zheng, Haoxuan Chen, Yizhou Han,
• Abstract summary: Despite ASR technology being full-scale adopted by industry and for large portions of the population, ASR systems often have errors that require editors to post-edit text quality.<n>This paper introduces CEGER, a compact edit representation that was generated for highly accurate, efficient ASR post-editing.<n> CEGER achieves state-of-the-art accuracy, achieving the lowest word error rate (WER) versus full rewrite and prior compact representations.
• Score: 3.219880761967806
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite ASR technology being full-scale adopted by industry and for large portions of the population, ASR systems often have errors that require editors to post-edit text quality. While LLMs are powerful post-editing tools, baseline full rewrite models have inference inefficiencies because they often generate the same redundant text over and over again. Compact edit representations have existed but often lack the efficacy and context required for optimal accuracy. This paper introduces CEGER (Context-Enhanced Granular Edit Representation), a compact edit representation that was generated for highly accurate, efficient ASR post-editing. CEGER allows LLMs to generate a sequence of structured, fine-grained, contextually rich commands to modify the original ASR output. A separate expansion module deterministically reconstructs the corrected text based on the commands. Extensive experiments on the LibriSpeech dataset that were conducted, CEGER achieves state-of-the-art accuracy, achieving the lowest word error rate (WER) versus full rewrite and prior compact representations.

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