用语音辅助文本恢复阿拉伯语重音符号，更快更准。
Constrained CTC Decoding for Efficient Diacritic Restoration
- 基于CTC的非自回归方法，解码时加入字符级约束
- 在ArVoice和ClArTTS上误差率显著降低
- 适合需要高效准确恢复重音符号的研究者
本文研究阿拉伯语语音转写中的重音符号恢复问题。多数语音数据缺乏重音符号，限制了对细微语音差异的建模。近年来，语音模态被用于补充基于文本的重音恢复方法。我们提出一种基于连接时序分类（CTC）的高效非自回归语音到文本重音恢复方法。通过从无重音转写构建字符级重音化词网，并在解码中强制限制为合法重音实现，提升准确性。在古典阿拉伯语和现代标准阿拉伯语测试集（ArVoice 和 ClArTTS）上，相比计算复杂的多模态基线，本方法在两个数据集上均实现统计显著的重音错误率下降，证明其兼具性能与效率优势。
原文摘要 · Abstract (English)
In this work, we address diacritic restoration for Arabic speech transcripts. Most speech data are undiacritized, limiting the ability of modeling fine-grained phonological distinctions. The speech modality has recently been explored as a way to complement text-based diacritic restoration efforts. We propose an efficient non-autoregressive approach for speech-to-text diacritization based on Connectionist Temporal Classification (CTC). Our method incorporates hard constraints during decoding by constructing a character-level diacritization lattice from an undiacritized transcript and restricting hypotheses to valid diacritized realizations. We evaluate on Classical Arabic and Modern Standard Arabic test sets (namely, ArVoice and ClArTTS) against a more computationally-complex multi-modal diacritic restoration baseline, and show statistically significant reductions in diacritic error rates in both, demonstrating that the proposed approach offers both performance and efficiency gains.
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