# ASR Error Correction with Constrained Decoding on Operation Prediction

arXiv: https://arxiv.org/abs/2208.04641 · HTML: https://mlsanity.com/paper/2208.04641/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 27 references that are in the map.
- First posted on arXiv 9 Aug 2022.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [Listen, Attend and Spell](https://mlsanity.com/paper/1508.01211/) (2015)
- [Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces](https://mlsanity.com/paper/1805.10190/) (2018)
- [Semantic Parsing for Task Oriented Dialog using Hierarchical Representations](https://mlsanity.com/paper/1810.07942/) (2018)
- [Encode, Tag, Realize: High-Precision Text Editing](https://mlsanity.com/paper/1909.01187/) (2019)
- [Pre-trained Summarization Distillation](https://mlsanity.com/paper/2010.13002/) (2020)
- [A spelling correction model for end-to-end speech recognition](https://mlsanity.com/paper/1902.07178/) (2019)
- [FastCorrect: Fast Error Correction with Edit Alignment for Automatic Speech Recognition](https://mlsanity.com/paper/2105.03842/) (2021)
- [Joint Contextual Modeling for ASR Correction and Language Understanding](https://mlsanity.com/paper/2002.00750/) (2020)
- [Phoneme-BERT: Joint Language Modelling of Phoneme Sequence and ASR Transcript](https://mlsanity.com/paper/2102.00804/) (2021)
- [Learning ASR-Robust Contextualized Embeddings for Spoken Language Understanding](https://mlsanity.com/paper/1909.10861/) (2019)
- [BART based semantic correction for Mandarin automatic speech recognition system](https://mlsanity.com/paper/2104.05507/) (2021)
- [SARG: A Novel Semi Autoregressive Generator for Multi-turn Incomplete Utterance Restoration](https://mlsanity.com/paper/2008.01474/) (2020)
- [Hallucination of speech recognition errors with sequence to sequence learning](https://mlsanity.com/paper/2103.12258/) (2021)
- [Remember the context! ASR slot error correction through memorization](https://mlsanity.com/paper/2109.05092/) (2021)
- [Integrated Semantic and Phonetic Post-correction for Chinese Speech Recognition](https://mlsanity.com/paper/2111.08400/) (2021)
- [Robust Spoken Language Understanding with RL-based Value Error Recovery](https://mlsanity.com/paper/2009.03095/) (2020)

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

- [Generative Speech Recognition Error Correction with Large Language Models and Task-Activating Prompting](https://mlsanity.com/paper/2309.15649/) (2023)
- [ed-cec: improving rare word recognition using asr postprocessing based on error detection and context-aware error correction](https://mlsanity.com/paper/2310.05129/) (2023)
- [MathSpeech: Leveraging Small LMs for Accurate Conversion in Mathematical Speech-to-Formula](https://mlsanity.com/paper/2412.15655/) (2024)
- [HTEC: Human Transcription Error Correction](https://mlsanity.com/paper/2309.10089/) (2023)
- [Interventional Speech Noise Injection for ASR Generalizable Spoken Language Understanding](https://mlsanity.com/paper/2410.15609/) (2024)
- [PMF-CEC: Phoneme-augmented Multimodal Fusion for Context-aware ASR Error Correction with Error-specific Selective Decoding](https://mlsanity.com/paper/2506.11064/) (2025)

## Abstract (arXiv)

Error correction techniques remain effective to refine outputs from automatic speech recognition (ASR) models. Existing end-to-end error correction methods based on an encoder-decoder architecture process all tokens in the decoding phase, creating undesirable latency. In this paper, we propose an ASR error correction method utilizing the predictions of correction operations. More specifically, we construct a predictor between the encoder and the decoder to learn if a token should be kept ("K"), deleted ("D"), or changed ("C") to restrict decoding to only part of the input sequence embeddings (the "C" tokens) for fast inference. Experiments on three public datasets demonstrate the effectiveness of the proposed approach in reducing the latency of the decoding process in ASR correction. It enhances the inference speed by at least three times (3.4 and 5.7 times) while maintaining the same level of accuracy (with WER reductions of 0.53% and 1.69% respectively) for our two proposed models compared to a solid encoder-decoder baseline. In the meantime, we produce and release a benchmark dataset contributing to the ASR error correction community to foster research along this line.