# Better Intermediates Improve CTC Inference Source: [Better Intermediates Improve CTC Inference]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:06:48.603Z Authors: Tatsuya Komatsu, Yusuke Fujita, Jaesong Lee, Lukas Lee, Shinji Watanabe, Yusuke Kida Published: 2022-04-01T02:51:23.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Speech Recognition on Librispeech (100 hours) and AISHELL v1](): Speech Recognition; Librispeech (100 hours) and AISHELL v1; WER (English): 7.11. ## Original sources and implementation [Original paper]() [PDF]()