# Spell my name: keyword boosted speech recognition

arXiv: https://arxiv.org/abs/2110.02791 · HTML: https://mlsanity.com/paper/2110.02791/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 27 references that are in the map.
- First posted on arXiv 6 Oct 2021.

## Builds on (most cited first, 16 of 16)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [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)
- [Regularizing and Optimizing LSTM Language Models](https://mlsanity.com/paper/1708.02182/) (2017)
- [Pushing the Limits of Semi-Supervised Learning for Automatic Speech Recognition](https://mlsanity.com/paper/2010.10504/) (2020)
- [On Using Monolingual Corpora in Neural Machine Translation](https://mlsanity.com/paper/1503.03535/) (2015)
- [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)
- [ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Context](https://mlsanity.com/paper/2005.03191/) (2020)
- [Contextual RNN-T For Open Domain ASR](https://mlsanity.com/paper/2006.03411/) (2020)
- [SpeechStew: Simply Mix All Available Speech Recognition Data to Train One Large Neural Network](https://mlsanity.com/paper/2104.02133/) (2021)
- [Deep Shallow Fusion for RNN-T Personalization](https://mlsanity.com/paper/2011.07754/) (2020)
- [Simple Fusion: Return of the Language Model](https://mlsanity.com/paper/1809.00125/) (2018)
- [Contextual Speech Recognition with Difficult Negative Training Examples](https://mlsanity.com/paper/1810.12170/) (2018)
- [End-to-end Speech Recognition with Word-based RNN Language Models](https://mlsanity.com/paper/1808.02608/) (2018)
- [Fast and Robust Unsupervised Contextual Biasing for Speech Recognition](https://mlsanity.com/paper/2005.01677/) (2020)

## Built on by (most cited first, 10 of 10)

- [Fast Context-Biasing for CTC and Transducer ASR models with CTC-based Word Spotter](https://mlsanity.com/paper/2406.07096/) (2024)
- [NGPU-LM: GPU-Accelerated N-Gram Language Model for Context-Biasing in Greedy ASR Decoding](https://mlsanity.com/paper/2505.22857/) (2025)
- [InterBiasing: Boost Unseen Word Recognition through Biasing Intermediate Predictions](https://mlsanity.com/paper/2406.14890/) (2024)
- [TurboBias: Universal ASR Context-Biasing powered by GPU-accelerated Phrase-Boosting Tree](https://mlsanity.com/paper/2508.07014/) (2025)
- [WCTC-Biasing: Retraining-free Contextual Biasing ASR with Wildcard CTC-based Keyword Spotting and Inter-layer Biasing](https://mlsanity.com/paper/2506.01263/) (2025)
- [Deep CLAS: Deep Contextual Listen, Attend and Spell](https://mlsanity.com/paper/2409.17603/) (2024)
- [Fast Streaming Transducer ASR Prototyping via Knowledge Distillation with Whisper](https://mlsanity.com/paper/2409.13499/) (2024)
- [Unifying Global and Near-Context Biasing in a Single Trie Pass](https://mlsanity.com/paper/2409.13514/) (2024)
- [N-gram Boosting: Improving Contextual Biasing with Normalized N-gram Targets](https://mlsanity.com/paper/2308.02092/) (2023)
- [TurboBias 2.0: Streaming Context-Biasing for Production-Efficient ASR Systems](https://mlsanity.com/paper/2608.21343/) (2026)

## Abstract (arXiv)

Recognition of uncommon words such as names and technical terminology is important to understanding conversations in context. However, the ability to recognise such words remains a challenge in modern automatic speech recognition (ASR) systems. In this paper, we propose a simple but powerful ASR decoding method that can better recognise these uncommon keywords, which in turn enables better readability of the results. The method boosts the probabilities of given keywords in a beam search based on acoustic model predictions. The method does not require any training in advance. We demonstrate the effectiveness of our method on the LibriSpeeech test sets and also internal data of real-world conversations. Our method significantly boosts keyword accuracy on the test sets, while maintaining the accuracy of the other words, and as well as providing significant qualitative improvements. This method is applicable to other tasks such as machine translation, or wherever unseen and difficult keywords need to be recognised in beam search.