# Better Intermediates Improve CTC Inference

arXiv: https://arxiv.org/abs/2204.00176 · HTML: https://mlsanity.com/paper/2204.00176/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 31 references that are in the map.
- First posted on arXiv 1 Apr 2022.

## Builds on (most cited first, 22 of 22)

- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [ESPnet: End-to-End Speech Processing Toolkit](https://mlsanity.com/paper/1804.00015/) (2018)
- [Non-Autoregressive Neural Machine Translation](https://mlsanity.com/paper/1711.02281/) (2017)
- [Deep Speech 2: End-to-End Speech Recognition in English and Mandarin](https://mlsanity.com/paper/1512.02595/) (2015)
- [Attention-Based Models for Speech Recognition](https://mlsanity.com/paper/1506.07503/) (2015)
- [Parallel WaveNet: Fast High-Fidelity Speech Synthesis](https://mlsanity.com/paper/1711.10433/) (2017)
- [A Comparative Study on Transformer vs RNN in Speech Applications](https://mlsanity.com/paper/1909.06317/) (2019)
- [Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative Refinement](https://mlsanity.com/paper/1802.06901/) (2018)
- [Recent Developments on ESPnet Toolkit Boosted by Conformer](https://mlsanity.com/paper/2010.13956/) (2020)
- [Intermediate Loss Regularization for CTC-based Speech Recognition](https://mlsanity.com/paper/2102.03216/) (2021)
- [Mask CTC: Non-Autoregressive End-to-End ASR with CTC and Mask Predict](https://mlsanity.com/paper/2005.08700/) (2020)
- [Imputer: Sequence Modelling via Imputation and Dynamic Programming](https://mlsanity.com/paper/2002.08926/) (2020)
- [Relaxing the Conditional Independence Assumption of CTC-based ASR by Conditioning on Intermediate Predictions](https://mlsanity.com/paper/2104.02724/) (2021)
- [Latent-Variable Non-Autoregressive Neural Machine Translation with Deterministic Inference Using a Delta Posterior](https://mlsanity.com/paper/1908.07181/) (2019)
- [Align-Refine: Non-Autoregressive Speech Recognition via Iterative Realignment](https://mlsanity.com/paper/2010.14233/) (2020)
- [Multitask Learning with Low-Level Auxiliary Tasks for Encoder-Decoder Based Speech Recognition](https://mlsanity.com/paper/1704.01631/) (2017)
- [First-Pass Large Vocabulary Continuous Speech Recognition using Bi-Directional Recurrent DNNs](https://mlsanity.com/paper/1408.2873/) (2014)
- [Deja-vu: Double Feature Presentation and Iterated Loss in Deep Transformer Networks](https://mlsanity.com/paper/1910.10324/) (2019)
- [Searchable Hidden Intermediates for End-to-End Models of Decomposable Sequence Tasks](https://mlsanity.com/paper/2105.00573/) (2021)
- [Hierarchical Multi Task Learning With CTC](https://mlsanity.com/paper/1807.07104/) (2018)
- [Hierarchical Multitask Learning for CTC-based Speech Recognition](https://mlsanity.com/paper/1807.06234/) (2018)

## Built on by (most cited first, 5 of 5)

- [FERERO: A Flexible Framework for Preference-Guided Multi-Objective Learning](https://mlsanity.com/paper/2412.01773/) (2024)
- [InterBiasing: Boost Unseen Word Recognition through Biasing Intermediate Predictions](https://mlsanity.com/paper/2406.14890/) (2024)
- [Rapid Language Adaptation for Multilingual E2E Speech Recognition Using Encoder Prompting](https://mlsanity.com/paper/2406.12611/) (2024)
- [Whale: Large-Scale multilingual ASR model with w2v-BERT and E-Branchformer with large speech data](https://mlsanity.com/paper/2506.01439/) (2025)
- [InterMPL: Momentum Pseudo-Labeling with Intermediate CTC Loss](https://mlsanity.com/paper/2211.00795/) (2022)

## Abstract (arXiv)

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.