# A spelling correction model for end-to-end speech recognition

arXiv: https://arxiv.org/abs/1902.07178 · HTML: https://mlsanity.com/paper/1902.07178/

## Key facts

- Cited by 63 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Feb 2019.

## Built on by (most cited first, 30 of 63)

- [Recent Advances in End-to-End Automatic Speech Recognition](https://mlsanity.com/paper/2111.01690/) (2021)
- [Generative Speech Recognition Error Correction with Large Language Models and Task-Activating Prompting](https://mlsanity.com/paper/2309.15649/) (2023)
- [Can Generative Large Language Models Perform ASR Error Correction?](https://mlsanity.com/paper/2307.04172/) (2023)
- [Whispering LLaMA: A Cross-Modal Generative Error Correction Framework for Speech Recognition](https://mlsanity.com/paper/2310.06434/) (2023)
- [FastCorrect: Fast Error Correction with Edit Alignment for Automatic Speech Recognition](https://mlsanity.com/paper/2105.03842/) (2021)
- [N-best T5: Robust ASR Error Correction using Multiple Input Hypotheses and Constrained Decoding Space](https://mlsanity.com/paper/2303.00456/) (2023)
- [Large Language Models are Efficient Learners of Noise-Robust Speech Recognition](https://mlsanity.com/paper/2401.10446/) (2024)
- [Transformer Based Deliberation for Two-Pass Speech Recognition](https://mlsanity.com/paper/2101.11577/) (2021)
- [ASR Error Correction using Large Language Models](https://mlsanity.com/paper/2409.09554/) (2024)
- [Neural Inverse Text Normalization](https://mlsanity.com/paper/2102.06380/) (2021)
- [Towards Contextual Spelling Correction for Customization of End-to-end Speech Recognition Systems](https://mlsanity.com/paper/2203.00888/) (2022)
- [Towards the Unseen: Iterative Text Recognition by Distilling from Errors](https://mlsanity.com/paper/2107.12081/) (2021)
- [A Light-weight contextual spelling correction model for customizing transducer-based speech recognition systems](https://mlsanity.com/paper/2108.07493/) (2021)
- [VarCLR: Variable Semantic Representation Pre-training via Contrastive Learning](https://mlsanity.com/paper/2112.02650/) (2021)
- [Large Language Model Based Generative Error Correction: A Challenge and Baselines for Speech Recognition, Speaker Tagging, and Emotion Recognition](https://mlsanity.com/paper/2409.09785/) (2024)
- [Sentence-Select: Large-Scale Language Model Data Selection for Rare-Word Speech Recognition](https://mlsanity.com/paper/2203.05008/) (2022)
- [Improving Deliberation by Text-Only and Semi-Supervised Training](https://mlsanity.com/paper/2206.14716/) (2022)
- [Failing Forward: Improving Generative Error Correction for ASR with Synthetic Data and Retrieval Augmentation](https://mlsanity.com/paper/2410.13198/) (2024)
- [Improving Contextual Spelling Correction by External Acoustics Attention and Semantic Aware Data Augmentation](https://mlsanity.com/paper/2302.11192/) (2023)
- [Crossmodal ASR Error Correction with Discrete Speech Units](https://mlsanity.com/paper/2405.16677/) (2024)
- [Error-driven Pruning of Language Models for Virtual Assistants](https://mlsanity.com/paper/2102.07219/) (2021)
- [MCSCSet: A Specialist-annotated Dataset for Medical-domain Chinese Spelling Correction](https://mlsanity.com/paper/2210.11720/) (2022)
- [Approximate Nearest Neighbour Phrase Mining for Contextual Speech Recognition](https://mlsanity.com/paper/2304.08862/) (2023)
- [Measuring the Effect of Transcription Noise on Downstream Language Understanding Tasks](https://mlsanity.com/paper/2502.13645/) (2025)
- [Aligning ASR Evaluation with Human and LLM Judgments: Intelligibility Metrics Using Phonetic, Semantic, and NLI Approaches](https://mlsanity.com/paper/2506.16528/) (2025)
- [Cross-Modal ASR Post-Processing System for Error Correction and Utterance Rejection](https://mlsanity.com/paper/2201.03313/) (2022)
- [ASR Error Correction with Constrained Decoding on Operation Prediction](https://mlsanity.com/paper/2208.04641/) (2022)
- [Non-autoregressive Error Correction for CTC-based ASR with Phone-conditioned Masked LM](https://mlsanity.com/paper/2209.04062/) (2022)
- [Unsupervised domain adaptation for speech recognition with unsupervised error correction](https://mlsanity.com/paper/2209.12043/) (2022)
- [Optimizing Two-Pass Cross-Lingual Transfer Learning: Phoneme Recognition and Phoneme to Grapheme Translation](https://mlsanity.com/paper/2312.03312/) (2023)

## Abstract (arXiv)

Attention-based sequence-to-sequence models for speech recognition jointly train an acoustic model, language model (LM), and alignment mechanism using a single neural network and require only parallel audio-text pairs. Thus, the language model component of the end-to-end model is only trained on transcribed audio-text pairs, which leads to performance degradation especially on rare words. While there have been a variety of work that look at incorporating an external LM trained on text-only data into the end-to-end framework, none of them have taken into account the characteristic error distribution made by the model. In this paper, we propose a novel approach to utilizing text-only data, by training a spelling correction (SC) model to explicitly correct those errors. On the LibriSpeech dataset, we demonstrate that the proposed model results in an 18.6% relative improvement in WER over the baseline model when directly correcting top ASR hypothesis, and a 29.0% relative improvement when further rescoring an expanded n-best list using an external LM.