# Contextual Biasing with the Knuth-Morris-Pratt Matching Algorithm

arXiv: https://arxiv.org/abs/2310.00178 · HTML: https://mlsanity.com/paper/2310.00178/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 47 references that are in the map.
- First posted on arXiv 29 Sep 2023.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://mlsanity.com/paper/1901.02860/) (2019)
- [Listen, Attend and Spell](https://mlsanity.com/paper/1508.01211/) (2015)
- [Hopfield Networks is All You Need](https://mlsanity.com/paper/2008.02217/) (2020)
- [Attention-Based Models for Speech Recognition](https://mlsanity.com/paper/1506.07503/) (2015)
- [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)
- [Towards better decoding and language model integration in sequence to sequence models](https://mlsanity.com/paper/1612.02695/) (2016)
- [Context-Aware Transformer Transducer for Speech Recognition](https://mlsanity.com/paper/2111.03250/) (2021)
- [Metalearned Neural Memory](https://mlsanity.com/paper/1907.09720/) (2019)
- [Class LM and word mapping for contextual biasing in End-to-End ASR](https://mlsanity.com/paper/2007.05609/) (2020)
- [Improving End-to-End Contextual Speech Recognition with Fine-Grained Contextual Knowledge Selection](https://mlsanity.com/paper/2201.12806/) (2022)
- [Tied & Reduced RNN-T Decoder](https://mlsanity.com/paper/2109.07513/) (2021)
- [Contextual Speech Recognition with Difficult Negative Training Examples](https://mlsanity.com/paper/1810.12170/) (2018)
- [Fast Contextual Adaptation with Neural Associative Memory for On-Device Personalized Speech Recognition](https://mlsanity.com/paper/2110.02220/) (2021)
- [Pseudo Label Is Better Than Human Label](https://mlsanity.com/paper/2203.12668/) (2022)
- [Robust Acoustic and Semantic Contextual Biasing in Neural Transducers for Speech Recognition](https://mlsanity.com/paper/2305.05271/) (2023)
- [Adaptive Contextual Biasing for Transducer Based Streaming Speech Recognition](https://mlsanity.com/paper/2306.00804/) (2023)

## Built on by (most cited first, 9 of 9)

- [Improving Neural Biasing for Contextual Speech Recognition by Early Context Injection and Text Perturbation](https://mlsanity.com/paper/2407.10303/) (2024)
- [TurboBias: Universal ASR Context-Biasing powered by GPU-accelerated Phrase-Boosting Tree](https://mlsanity.com/paper/2508.07014/) (2025)
- [Text Injection for Neural Contextual Biasing](https://mlsanity.com/paper/2406.02921/) (2024)
- [Evaluating ASR robustness to spontaneous speech errors: A study of WhisperX using a Speech Error Database](https://mlsanity.com/paper/2508.13060/) (2025)
- [Self-consistent context aware conformer transducer for speech recognition](https://mlsanity.com/paper/2402.06592/) (2024)
- [Unifying Global and Near-Context Biasing in a Single Trie Pass](https://mlsanity.com/paper/2409.13514/) (2024)
- [PHRASED: Phrase Dictionary Biasing for Speech Translation](https://mlsanity.com/paper/2506.09175/) (2025)
- [A Neural Model for Contextual Biasing Score Learning and Filtering](https://mlsanity.com/paper/2510.23849/) (2025)
- [LOGIC: Efficient and Robust Contextual Biasing for Speech LLMs via Logit-Space Integration](https://mlsanity.com/paper/2601.15397/) (2026)

## Abstract (arXiv)

Contextual biasing refers to the problem of biasing the automatic speech recognition (ASR) systems towards rare entities that are relevant to the specific user or application scenarios. We propose algorithms for contextual biasing based on the Knuth-Morris-Pratt algorithm for pattern matching. During beam search, we boost the score of a token extension if it extends matching into a set of biasing phrases. Our method simulates the classical approaches often implemented in the weighted finite state transducer (WFST) framework, but avoids the FST language altogether, with careful considerations on memory footprint and efficiency on tensor processing units (TPUs) by vectorization. Without introducing additional model parameters, our method achieves significant word error rate (WER) reductions on biasing test sets by itself, and yields further performance gain when combined with a model-based biasing method.