# Towards Contextual Spelling Correction for Customization of End-to-end Speech Recognition Systems

arXiv: https://arxiv.org/abs/2203.00888 · HTML: https://mlsanity.com/paper/2203.00888/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 41 references that are in the map.
- First posted on arXiv 2 Mar 2022.

## Builds on (most cited first, 30 of 31)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Listen, Attend and Spell](https://mlsanity.com/paper/1508.01211/) (2015)
- [A Survey on Deep Learning for Named Entity Recognition](https://mlsanity.com/paper/1812.09449/) (2018)
- [Transformer Transducer: A Streamable Speech Recognition Model with Transformer Encoders and RNN-T Loss](https://mlsanity.com/paper/2002.02562/) (2020)
- [Streaming End-to-end Speech Recognition For Mobile Devices](https://mlsanity.com/paper/1811.06621/) (2018)
- [Recent Advances in End-to-End Automatic Speech Recognition](https://mlsanity.com/paper/2111.01690/) (2021)
- [Deep context: end-to-end contextual speech recognition](https://mlsanity.com/paper/1808.02480/) (2018)
- [Contextualized Streaming End-to-End Speech Recognition with Trie-Based Deep Biasing and Shallow Fusion](https://mlsanity.com/paper/2104.02194/) (2021)
- [A Streaming On-Device End-to-End Model Surpassing Server-Side Conventional Model Quality and Latency](https://mlsanity.com/paper/2003.12710/) (2020)
- [Developing Real-time Streaming Transformer Transducer for Speech Recognition on Large-scale Dataset](https://mlsanity.com/paper/2010.11395/) (2020)
- [Contextual RNN-T For Open Domain ASR](https://mlsanity.com/paper/2006.03411/) (2020)
- [Transformer-Transducer: End-to-End Speech Recognition with Self-Attention](https://mlsanity.com/paper/1910.12977/) (2019)
- [Improving RNN Transducer Modeling for End-to-End Speech Recognition](https://mlsanity.com/paper/1909.12415/) (2019)
- [Deep Shallow Fusion for RNN-T Personalization](https://mlsanity.com/paper/2011.07754/) (2020)
- [A spelling correction model for end-to-end speech recognition](https://mlsanity.com/paper/1902.07178/) (2019)
- [Developing RNN-T Models Surpassing High-Performance Hybrid Models with Customization Capability](https://mlsanity.com/paper/2007.15188/) (2020)
- [Deliberation Model Based Two-Pass End-to-End Speech Recognition](https://mlsanity.com/paper/2003.07962/) (2020)
- [Correction of Automatic Speech Recognition with Transformer Sequence-to-sequence Model](https://mlsanity.com/paper/1910.10697/) (2019)
- [FastCorrect: Fast Error Correction with Edit Alignment for Automatic Speech Recognition](https://mlsanity.com/paper/2105.03842/) (2021)
- [A New Training Pipeline for an Improved Neural Transducer](https://mlsanity.com/paper/2005.09319/) (2020)
- [Tree-constrained Pointer Generator for End-to-end Contextual Speech Recognition](https://mlsanity.com/paper/2109.00627/) (2021)
- [Instant One-Shot Word-Learning for Context-Specific Neural Sequence-to-Sequence Speech Recognition](https://mlsanity.com/paper/2107.02268/) (2021)
- [DelightfulTTS: The Microsoft Speech Synthesis System for Blizzard Challenge 2021](https://mlsanity.com/paper/2110.12612/) (2021)
- [Deliberation of Streaming RNN-Transducer by Non-autoregressive Decoding](https://mlsanity.com/paper/2112.11442/) (2021)
- [A Light-weight contextual spelling correction model for customizing transducer-based speech recognition systems](https://mlsanity.com/paper/2108.07493/) (2021)

## Built on by (most cited first, 17 of 17)

- [Can Contextual Biasing Remain Effective with Whisper and GPT-2?](https://mlsanity.com/paper/2306.01942/) (2023)
- [Contextualized Automatic Speech Recognition with Attention-Based Bias Phrase Boosted Beam Search](https://mlsanity.com/paper/2401.10449/) (2024)
- [Improving Neural Biasing for Contextual Speech Recognition by Early Context Injection and Text Perturbation](https://mlsanity.com/paper/2407.10303/) (2024)
- [Contextualized End-to-end Automatic Speech Recognition with Intermediate Biasing Loss](https://mlsanity.com/paper/2406.16120/) (2024)
- [Improving Contextual Spelling Correction by External Acoustics Attention and Semantic Aware Data Augmentation](https://mlsanity.com/paper/2302.11192/) (2023)
- [Contextualized Automatic Speech Recognition with Dynamic Vocabulary](https://mlsanity.com/paper/2405.13344/) (2024)
- [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)
- [SpellMapper: A non-autoregressive neural spellchecker for ASR customization with candidate retrieval based on n-gram mappings](https://mlsanity.com/paper/2306.02317/) (2023)
- [AdaCS: Adaptive Normalization for Enhanced Code-Switching ASR](https://mlsanity.com/paper/2501.07102/) (2025)
- [PERCORE: A Deep Learning-Based Framework for Persian Spelling Correction with Phonetic Analysis](https://mlsanity.com/paper/2407.14789/) (2024)
- [Spelling Correction through Rewriting of Non-Autoregressive ASR Lattices](https://mlsanity.com/paper/2409.16469/) (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)
- [Incorporating Class-based Language Model for Named Entity Recognition in Factorized Neural Transducer](https://mlsanity.com/paper/2309.07648/) (2023)
- [Wiki-En-ASR-Adapt: Large-scale synthetic dataset for English ASR Customization](https://mlsanity.com/paper/2309.17267/) (2023)
- [High-precision Voice Search Query Correction via Retrievable Speech-text Embedings](https://mlsanity.com/paper/2401.04235/) (2024)
- [Improving the quality of Persian clinical text with a novel spelling correction system](https://mlsanity.com/paper/2408.03622/) (2024)
- [Automatic Text Pronunciation Correlation Generation and Application for Contextual Biasing](https://mlsanity.com/paper/2501.00804/) (2025)

## Abstract (arXiv)

Contextual biasing is an important and challenging task for end-to-end automatic speech recognition (ASR) systems, which aims to achieve better recognition performance by biasing the ASR system to particular context phrases such as person names, music list, proper nouns, etc. Existing methods mainly include contextual LM biasing and adding bias encoder into end-to-end ASR models. In this work, we introduce a novel approach to do contextual biasing by adding a contextual spelling correction model on top of the end-to-end ASR system. We incorporate contextual information into a sequence-to-sequence spelling correction model with a shared context encoder. Our proposed model includes two different mechanisms: autoregressive (AR) and non-autoregressive (NAR). We propose filtering algorithms to handle large-size context lists, and performance balancing mechanisms to control the biasing degree of the model. We demonstrate the proposed model is a general biasing solution which is domain-insensitive and can be adopted in different scenarios. Experiments show that the proposed method achieves as much as 51% relative word error rate (WER) reduction over ASR system and outperforms traditional biasing methods. Compared to the AR solution, the proposed NAR model reduces model size by 43.2% and speeds up inference by 2.1 times.