用拼音增强重述模型，精准修正中文语音识别错误
PERL: Pinyin Enhanced Rephrasing Language Model for Chinese ASR N-best Error Correction
- 通过拼音与语义融合的门控机制，动态平衡发音相似性与语义合理性
- 在Aishell-1上降低29.11%的词错误率，新数据集上最高降70%
- 适合需要低延迟、高精度的中文语音纠错场景
中文语音识别纠错困难，因错误常为发音相似（多字同拼音），且需在嘈杂的N-best候选中满足长度约束。现有方法或仅在提示/特征层使用拼音，未融入模型表示；或依赖生成解码，易偏离长度。本文提出PERL，一种约束式重述管道：(i) 预测目标长度并通过掩码预算强制执行；(ii) 通过基于句子语义的逐标记门控，融合语义与发音（拼音）表示。在Aishell-1和新构建的领域N-best基准DoAD上的实验表明，PERL持续降低词错误率（Aishell-1上29.11%，DoAD上最高约70%），同时保持低延迟。还分析了长度泛化与发音-语义交互，揭示PERL在何种情况下依赖发音线索或语义约束。
原文摘要 · Abstract (English)
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.
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