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

• URL: http://arxiv.org/abs/2412.03230v2
• Date: Mon, 22 Sep 2025 07:21:41 GMT
• Title: PERL: Pinyin Enhanced Rephrasing Language Model for Chinese ASR N-best Error Correction
• Authors: Junhong Liang, Bojun Zhang,
• Abstract summary: Existing Chinese ASR correction methods have not effectively utilized Pinyin information, a unique feature of the Chinese language.<n>We propose a textbfPinyin textbfEnhanced textbfRephrasing textbfLanguage model (PERL) pipeline, designed explicitly for N-best correction scenarios.
• Score: 0.742779257315787
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing Chinese ASR correction methods have not effectively utilized Pinyin information, a unique feature of the Chinese language. In this study, we address this gap by proposing a \textbf{P}inyin \textbf{E}nhanced \textbf{R}ephrasing \textbf{L}anguage model (PERL) pipeline, designed explicitly for N-best correction scenarios. 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 on Aishell-1 and around 70\% CER reduction on domain-specific datasets. PERL predicts the correct length of the output, leveraging the Pinyin information, which is embedded with a semantic model to perform phonetically similar corrections. Extensive experiments demonstrate the effectiveness of correcting wrong characters using N-best output and the low latency of our model.

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