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

Chinese ASR correction is challenging because errors are often phonetic (many characters share similar Pinyin) while the correction model must also obey a length constraint under noisy N-best hypotheses. Existing approaches either exploit Pinyin only at the prompt/feature level without integrating it into model representations or rely on generative decoding that can drift in length. We propose PERL, a constrained rephrasing pipeline for Chinese N-best ASR correction that (i) predicts the target length and enforces it via mask budgeting, and (ii) fuses semantic and phonetic (Pinyin) representations through token-wise gates conditioned on sentence semantics. Experiments on Aishell-1 and our new domain N-best benchmark DoAD show that PERL consistently reduces CER (29.11% on Aishell-1 and up to ~70% on DoAD) while maintaining low latency. We also provide analyzes of length generalization and phonetic--semantic interactions, showing when PERL relies on phonetic cues versus semantic constraints.