# Improving Contextual Recognition of Rare Words with an Alternate Spelling Prediction Model

arXiv: https://arxiv.org/abs/2209.01250 · HTML: https://mlsanity.com/paper/2209.01250/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 22 references that are in the map.
- First posted on arXiv 2 Sep 2022.

## Builds on (most cited first, 13 of 13)

- [Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates](https://mlsanity.com/paper/1804.10959/) (2018)
- [GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed Audio](https://mlsanity.com/paper/2106.06909/) (2021)
- [Deep context: end-to-end contextual speech recognition](https://mlsanity.com/paper/1808.02480/) (2018)
- [WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit](https://mlsanity.com/paper/2102.01547/) (2021)
- [Contextualized Streaming End-to-End Speech Recognition with Trie-Based Deep Biasing and Shallow Fusion](https://mlsanity.com/paper/2104.02194/) (2021)
- [Contextual RNN-T For Open Domain ASR](https://mlsanity.com/paper/2006.03411/) (2020)
- [Deep Shallow Fusion for RNN-T Personalization](https://mlsanity.com/paper/2011.07754/) (2020)
- [Earnings-21: A Practical Benchmark for ASR in the Wild](https://mlsanity.com/paper/2104.11348/) (2021)
- [Class LM and word mapping for contextual biasing in End-to-End ASR](https://mlsanity.com/paper/2007.05609/) (2020)
- [Tree-constrained Pointer Generator for End-to-end Contextual Speech Recognition](https://mlsanity.com/paper/2109.00627/) (2021)
- [Contextual Speech Recognition with Difficult Negative Training Examples](https://mlsanity.com/paper/1810.12170/) (2018)
- [End-to-end contextual speech recognition using class language models and a token passing decoder](https://mlsanity.com/paper/1812.02142/) (2018)
- [G2G: TTS-Driven Pronunciation Learning for Graphemic Hybrid ASR](https://mlsanity.com/paper/1910.12612/) (2019)

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

- [Contextualized End-to-End Speech Recognition with Contextual Phrase Prediction Network](https://mlsanity.com/paper/2305.12493/) (2023)
- [Adaptive Contextual Biasing for Transducer Based Streaming Speech Recognition](https://mlsanity.com/paper/2306.00804/) (2023)
- [Improving Neural Biasing for Contextual Speech Recognition by Early Context Injection and Text Perturbation](https://mlsanity.com/paper/2407.10303/) (2024)
- [Fast Context-Biasing for CTC and Transducer ASR models with CTC-based Word Spotter](https://mlsanity.com/paper/2406.07096/) (2024)
- [SpellMapper: A non-autoregressive neural spellchecker for ASR customization with candidate retrieval based on n-gram mappings](https://mlsanity.com/paper/2306.02317/) (2023)
- [Implementing contextual biasing in GPU decoder for online ASR](https://mlsanity.com/paper/2306.15685/) (2023)
- [Locality enhanced dynamic biasing and sampling strategies for contextual ASR](https://mlsanity.com/paper/2401.13146/) (2024)
- [IndicContextEval: A Benchmark for Evaluating Context Utilisation in Audio Large Language Models Across 8 Indic Languages](https://mlsanity.com/paper/2606.19157/) (2026)
- [Quantification of stylistic differences in human- and ASR-produced transcripts of African American English](https://mlsanity.com/paper/2409.03059/) (2024)
- [Automatic Speech Recognition of African American English: Lexical and Contextual Effects](https://mlsanity.com/paper/2506.06888/) (2025)
- [Contextual Earnings-22: A Speech Recognition Benchmark with Custom Vocabulary in the Wild](https://mlsanity.com/paper/2604.07354/) (2026)
- [Post-decoder Biasing for End-to-End Speech Recognition of Multi-turn Medical Interview](https://mlsanity.com/paper/2403.00370/) (2024)
- [Unifying Global and Near-Context Biasing in a Single Trie Pass](https://mlsanity.com/paper/2409.13514/) (2024)
- [Wiki-En-ASR-Adapt: Large-scale synthetic dataset for English ASR Customization](https://mlsanity.com/paper/2309.17267/) (2023)
- [Speaker Tagging Correction With Non-Autoregressive Language Models](https://mlsanity.com/paper/2409.00151/) (2024)
- [Peeking Into The Future For Contextual Biasing](https://mlsanity.com/paper/2512.17657/) (2025)
- [RECOVER: Robust Entity Correction via agentic Orchestration of hypothesis Variants for Evidence-based Recovery](https://mlsanity.com/paper/2603.16411/) (2026)

## Abstract (arXiv)

Contextual ASR, which takes a list of bias terms as input along with audio, has drawn recent interest as ASR use becomes more widespread. We are releasing contextual biasing lists to accompany the Earnings21 dataset, creating a public benchmark for this task. We present baseline results on this benchmark using a pretrained end-to-end ASR model from the WeNet toolkit. We show results for shallow fusion contextual biasing applied to two different decoding algorithms. Our baseline results confirm observations that end-to-end models struggle in particular with words that are rarely or never seen during training, and that existing shallow fusion techniques do not adequately address this problem. We propose an alternate spelling prediction model that improves recall of rare words by 34.7% relative and of out-of-vocabulary words by 97.2% relative, compared to contextual biasing without alternate spellings. This model is conceptually similar to ones used in prior work, but is simpler to implement as it does not rely on either a pronunciation dictionary or an existing text-to-speech system.