LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context

• URL: http://arxiv.org/abs/2505.17410v1
• Date: Fri, 23 May 2025 02:54:52 GMT
• Title: LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context
• Authors: Natsuo Yamashita, Masaaki Yamamoto, Hiroaki Kokubo, Yohei Kawaguchi,
• Abstract summary: We propose a novel GER approach that targets rare words and incorporates phonetic information.<n> Experimental results show that our method not only improves the correction of rare words but also reduces the WER and CER.
• Score: 4.444835399672951
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generative error correction (GER) with large language models (LLMs) has emerged as an effective post-processing approach to improve automatic speech recognition (ASR) performance. However, it often struggles with rare or domain-specific words due to limited training data. Furthermore, existing LLM-based GER approaches primarily rely on textual information, neglecting phonetic cues, which leads to over-correction. To address these issues, we propose a novel LLM-based GER approach that targets rare words and incorporates phonetic information. First, we generate synthetic data to contain rare words for fine-tuning the GER model. Second, we integrate ASR's N-best hypotheses along with phonetic context to mitigate over-correction. Experimental results show that our method not only improves the correction of rare words but also reduces the WER and CER across both English and Japanese datasets.

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