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

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 $\mathbf{\text{P}}$inyin $\mathbf{\text{E}}$nhanced $\mathbf{\text{R}}$ephrasing $\mathbf{\text{L}}$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.