PERL：用于中文语音识别N-best纠错的拼音增强改写语言模型
PERL: Pinyin Enhanced Rephrasing Language Model for Chinese ASR N-best Error Correction
AI总结：
针对中文语音识别纠错难题，提出PERL受限改写管道，通过预测目标长度、掩码预算及融合语义与拼音表示来纠错，在Aishell-1和DoAD实验中持续降低字符错误率且保持低延迟，还分析了相关交互。
摘要：
中文语音识别纠错具有挑战性，因为错误常是语音性的，且纠错模型在有噪声的N-best假设下须遵循长度约束。现有方法要么仅在提示/特征层面利用拼音，未将其整合到模型表示中，要么依赖会在长度上偏移的生成式解码。我们提出了PERL，一种用于中文N-best语音识别纠错的受限改写管道，它能预测目标长度并通过掩码预算来执行，还能通过基于句子语义的逐词门控融合语义和语音（拼音）表示。在Aishell-1和新的领域N-best基准DoAD上的实验表明，PERL能持续降低字符错误率，同时保持低延迟。我们还对长度泛化和语音-语义交互进行了分析，展示了PERL何时依赖语音线索与语义约束。
英文摘要：
Chinese ASR correction is challenging because errors are often \emph{phonetic} (many characters share similar Pinyin) while the correction model must also obey a \emph{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 \textbf{PERL}, a \textbf{constrained rephrasing pipeline} for Chinese N-best ASR correction that (i) predicts the target length and enforces it via mask budgeting, and (ii) fuses \emph{semantic} and \emph{phonetic} (Pinyin) representations through token-wise gates conditioned on sentence semantics. Experiments on Aishell-1 and our new domain N-best benchmark \textbf{DoAD} show that PERL consistently reduces CER (29.11\% on Aishell-1 and up to $\sim$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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
