标题： LCS-CTC：利用软对齐提高语音转录的鲁棒性 显示英文标题

标题： LCS-CTC: Leveraging Soft Alignments to Enhance Phonetic Transcription Robustness

摘要： 语音转录对于细粒度的语言分析和下游语音应用至关重要。虽然连接主义时间分类（CTC）由于其效率而被广泛用于此类任务，但在识别性能方面往往不足，尤其是在语音不清晰和不流畅的情况下。在本工作中，我们提出了LCS-CTC，这是一种用于音素级语音识别的两阶段框架，结合了感知相似性的局部对齐算法和约束的CTC训练目标。通过预测细粒度的帧-音素成本矩阵并应用修改的最长公共子序列（LCS）算法，我们的方法识别出高置信度的对齐区域，这些区域用于约束CTC解码路径空间，从而减少过拟合并提高泛化能力，这使得稳健的识别和无文本强制对齐成为可能。在LibriSpeech和PPA上的实验表明，LCS-CTC始终优于原始CTC基线，表明其在流畅和不流畅语音中统一音素建模的潜力。 显示英文摘要

摘要： 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.