This paper explores semi-supervised training for sequence tasks, such as Optical Character Recognition or Automatic Speech Recognition. We propose a novel loss function $\unicode{x2013}$ SoftCTC $\unicode{x2013}$ which is an extension of CTC allowing to consider multiple transcription variants at the same time. This allows to omit the confidence based filtering step which is otherwise a crucial component of pseudo-labeling approaches to semi-supervised learning. We demonstrate the effectiveness of our method on a challenging handwriting recognition task and conclude that SoftCTC matches the performance of a finely-tuned filtering based pipeline. We also evaluated SoftCTC in terms of computational efficiency, concluding that it is significantly more efficient than a na\"ive CTC-based approach for training on multiple transcription variants, and we make our GPU implementation public.

翻译：摘要：本文探索了序列任务（如光学字符识别或自动语音识别）中的半监督训练方法。我们提出了一种新型损失函数——SoftCTC——它是CTC的扩展，能够同时考虑多种转录变体。这使得我们可以省略基于置信度的过滤步骤，而该步骤通常是伪标签方法在半监督学习中的关键组成部分。我们在具有挑战性的手写识别任务上验证了该方法的有效性，并得出结论：SoftCTC的性能可与精细调优的过滤基线方法相媲美。我们还从计算效率角度评估了SoftCTC，发现其在多转录变体训练上显著优于基于CTC的朴素方法，并公开了我们的GPU实现。