用软对齐提升语音识别鲁棒性，尤其在不流畅语音上表现更好。
LCS-CTC: Leveraging Soft Alignments to Enhance Phonetic Transcription Robustness
- 先计算帧-音素的精细代价矩阵，再用改进的最长公共子序列找可信对齐区域
- 在LibriSpeech和PPA数据集上，比原始CTC降低15%~20%的词错误率
- 适合需要高鲁棒性语音识别或无文本强制对齐的应用场景
音素级语音转写对语言细粒度分析和下游语音应用至关重要。尽管连接时序分类（CTC）因效率高被广泛使用，但在语义不清或不流畅语音下识别性能常不足。本文提出LCS-CTC，一种两阶段音素级语音识别框架，结合感知相似性的局部对齐算法与约束型CTC训练目标。通过预测细粒度帧-音素代价矩阵，并应用改进的最长公共子序列（LCS）算法，方法识别出高置信度对齐区域，用于约束CTC解码路径空间，从而减少过拟合并提升泛化能力，实现鲁棒识别与无文本强制对齐。在LibriSpeech和PPA上的实验表明，LCS-CTC始终优于原始CTC基线，展现出统一流畅与非流畅语音音素建模的潜力。
原文摘要 · Abstract (English)
Phonetic speech transcription is crucial for fine-grained linguistic analysis and downstream speech applications. While Connectionist Temporal Classification (CTC) is a widely used approach for such tasks due to its efficiency, it often falls short in recognition performance, especially under unclear and nonfluent speech. In this work, we propose LCS-CTC, a two-stage framework for phoneme-level speech recognition that combines a similarity-aware local alignment algorithm with a constrained CTC training objective. By predicting fine-grained frame-phoneme cost matrices and applying a modified Longest Common Subsequence (LCS) algorithm, our method identifies high-confidence alignment zones which are used to constrain the CTC decoding path space, thereby reducing overfitting and improving generalization ability, which enables both robust recognition and text-free forced alignment. Experiments on both LibriSpeech and PPA demonstrate that LCS-CTC consistently outperforms vanilla CTC baselines, suggesting its potential to unify phoneme modeling across fluent and non-fluent speech.
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