This paper proposes a method for improved CTC inference with searched
intermediates and multi-pass conditioning. The paper first formulates
self-conditioned CTC as a probabilistic model with an intermediate prediction
as a latent representation and provides a tractable conditioning framework. We
then propose two new conditioning methods based on the new formulation: (1)
Searched intermediate conditioning that refines intermediate predictions with
beam-search, (2) Multi-pass conditioning that uses predictions of previous
inference for conditioning the next inference. These new approaches enable
better conditioning than the original self-conditioned CTC during inference and
improve the final performance. Experiments with the LibriSpeech dataset show
relative 3%/12% performance improvement at the maximum in test clean/other sets
compared to the original self-conditioned CTC.

本文提出了一种使用搜索的中间结果和多次条件的改进 CTC 推断方法，通过提出新的条件方法，如搜索中间结果和多次通过条件，使得 CTC 推断中的条件能力更强，相对于原本的自我条件的 CTC，在 LibriSpeech 数据集上测试表现有明显提高。