# Non-autoregressive Error Correction for CTC-based ASR with Phone-conditioned Masked LM

arXiv: https://arxiv.org/abs/2209.04062 · HTML: https://mlsanity.com/paper/2209.04062/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 39 references that are in the map.
- First posted on arXiv 8 Sep 2022.

## Builds on (most cited first, 25 of 25)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [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)
- [Listen, Attend and Spell](https://mlsanity.com/paper/1508.01211/) (2015)
- [Transformer Transducer: A Streamable Speech Recognition Model with Transformer Encoders and RNN-T Loss](https://mlsanity.com/paper/2002.02562/) (2020)
- [Levenshtein Transformer](https://mlsanity.com/paper/1905.11006/) (2019)
- [An analysis of incorporating an external language model into a sequence-to-sequence model](https://mlsanity.com/paper/1712.01996/) (2017)
- [Towards better decoding and language model integration in sequence to sequence models](https://mlsanity.com/paper/1612.02695/) (2016)
- [Mask CTC: Non-Autoregressive End-to-End ASR with CTC and Mask Predict](https://mlsanity.com/paper/2005.08700/) (2020)
- [A spelling correction model for end-to-end speech recognition](https://mlsanity.com/paper/1902.07178/) (2019)
- [Correction of Automatic Speech Recognition with Transformer Sequence-to-sequence Model](https://mlsanity.com/paper/1910.10697/) (2019)
- [ASR Error Correction and Domain Adaptation Using Machine Translation](https://mlsanity.com/paper/2003.07692/) (2020)
- [FastCorrect: Fast Error Correction with Edit Alignment for Automatic Speech Recognition](https://mlsanity.com/paper/2105.03842/) (2021)
- [Distilling the Knowledge of BERT for Sequence-to-Sequence ASR](https://mlsanity.com/paper/2008.03822/) (2020)
- [Improved Mask-CTC for Non-Autoregressive End-to-End ASR](https://mlsanity.com/paper/2010.13270/) (2020)
- [Fast End-to-End Speech Recognition via Non-Autoregressive Models and Cross-Modal Knowledge Transferring from BERT](https://mlsanity.com/paper/2102.07594/) (2021)
- [Listen Attentively, and Spell Once: Whole Sentence Generation via a Non-Autoregressive Architecture for Low-Latency Speech Recognition](https://mlsanity.com/paper/2005.04862/) (2020)
- [BART based semantic correction for Mandarin automatic speech recognition system](https://mlsanity.com/paper/2104.05507/) (2021)
- [Cascade RNN-Transducer: Syllable Based Streaming On-device Mandarin Speech Recognition with a Syllable-to-Character Converter](https://mlsanity.com/paper/2011.08469/) (2020)
- [ASR Rescoring and Confidence Estimation with ELECTRA](https://mlsanity.com/paper/2110.01857/) (2021)
- [Hierarchical Multitask Learning for CTC-based Speech Recognition](https://mlsanity.com/paper/1807.06234/) (2018)
- [Alignment Knowledge Distillation for Online Streaming Attention-based Speech Recognition](https://mlsanity.com/paper/2103.00422/) (2021)
- [Learn Spelling from Teachers: Transferring Knowledge from Language Models to Sequence-to-Sequence Speech Recognition](https://mlsanity.com/paper/1907.06017/) (2019)

## Built on by (most cited first, 6 of 6)

- [4D ASR: Joint modeling of CTC, Attention, Transducer, and Mask-Predict decoders](https://mlsanity.com/paper/2212.10818/) (2022)
- [Joint Beam Search Integrating CTC, Attention, and Transducer Decoders](https://mlsanity.com/paper/2406.02950/) (2024)
- [LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context](https://mlsanity.com/paper/2505.17410/) (2025)
- [Correction Focused Language Model Training for Speech Recognition](https://mlsanity.com/paper/2310.11003/) (2023)
- [PRoDeliberation: Parallel Robust Deliberation for End-to-End Spoken Language Understanding](https://mlsanity.com/paper/2406.07823/) (2024)
- [Merging the Knowledge of LLMs for Automatic Speech Recognition](https://mlsanity.com/paper/2609.15743/) (2026)

## Abstract (arXiv)

Connectionist temporal classification (CTC) -based models are attractive in automatic speech recognition (ASR) because of their non-autoregressive nature. To take advantage of text-only data, language model (LM) integration approaches such as rescoring and shallow fusion have been widely used for CTC. However, they lose CTC's non-autoregressive nature because of the need for beam search, which slows down the inference speed. In this study, we propose an error correction method with phone-conditioned masked LM (PC-MLM). In the proposed method, less confident word tokens in a greedy decoded output from CTC are masked. PC-MLM then predicts these masked word tokens given unmasked words and phones supplementally predicted from CTC. We further extend it to Deletable PC-MLM in order to address insertion errors. Since both CTC and PC-MLM are non-autoregressive models, the method enables fast LM integration. Experimental evaluations on the Corpus of Spontaneous Japanese (CSJ) and TED-LIUM2 in domain adaptation setting shows that our proposed method outperformed rescoring and shallow fusion in terms of inference speed, and also in terms of recognition accuracy on CSJ.