PERL: Pinyin Enhanced Rephrasing Language Model for Chinese ASR N-best Error Correction

• URL: http://arxiv.org/abs/2412.03230v1
• Date: Wed, 04 Dec 2024 11:28:52 GMT
• Title: PERL: Pinyin Enhanced Rephrasing Language Model for Chinese ASR N-best Error Correction
• Authors: Junhong Liang,
• Abstract summary: We propose a Pinyin Enhanced Rephrasing Language Model (PERL), specifically designed for N-best correction scenarios. We conduct experiments on the Aishell-1 dataset and our newly proposed DoAD dataset.
• Score: 0.0
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• Abstract: ASR correction methods have predominantly focused on general datasets and have not effectively utilized Pinyin information, unique to the Chinese language. In this study, we address this gap by proposing a Pinyin Enhanced Rephrasing Language Model (PERL), specifically designed for N-best correction scenarios. Additionally, we implement a length predictor module to address the variable-length problem. We conduct experiments on the Aishell-1 dataset and our newly proposed DoAD dataset. The results show that our approach outperforms baseline methods, achieving a 29.11% reduction in Character Error Rate (CER) on Aishell-1 and around 70% CER reduction on domain-specific datasets. Furthermore, our approach leverages Pinyin similarity at the token level, providing an advantage over baselines and leading to superior performance.

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