mlsanityData to 7 Oct 2026

Contextual Biasing with the Knuth-Morris-Pratt Matching Algorithm

arXiv 2310.00178 ↗First posted 29 Sep 2023cs.CL eess.AScited by 9

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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.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 21 of its 47 references that are in the map. First posted on arXiv 29 Sep 2023.

Builds on

21 of 47 references.

References · 21

  1. 2017Attention Is All You Needcited by 59,932
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  3. 2020Conformer: Convolution-augmented Transformer for Speech Recognitioncited by 1,838
  4. 2019Transformer-XL: Attentive Language Models Beyond a Fixed-Length Contextcited by 1,830
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  6. 2020Hopfield Networks is All You Needcited by 461
  7. 2015Attention-Based Models for Speech Recognitioncited by 400
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  10. 2016Towards better decoding and language model integration in sequence to sequence modelscited by 100
  11. 2021Contextualized Streaming End-to-End Speech Recognition with Trie-Based Deep Biasing and Shallow Fusioncited by 100
  12. 2021Context-Aware Transformer Transducer for Speech Recognitioncited by 56
9 more
  1. 2019Metalearned Neural Memorycited by 44
  2. 2020Class LM and word mapping for contextual biasing in End-to-End ASRcited by 42
  3. 2022Improving End-to-End Contextual Speech Recognition with Fine-Grained Contextual Knowledge Selectioncited by 39
  4. 2021Tied & Reduced RNN-T Decodercited by 35
  5. 2018Contextual Speech Recognition with Difficult Negative Training Examplescited by 28
  6. 2021Fast Contextual Adaptation with Neural Associative Memory for On-Device Personalized Speech Recognitioncited by 25
  7. 2022Pseudo Label Is Better Than Human Labelcited by 25
  8. 2023Robust Acoustic and Semantic Contextual Biasing in Neural Transducers for Speech Recognitioncited by 18
  9. 2023Adaptive Contextual Biasing for Transducer Based Streaming Speech Recognitioncited by 12

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 4; 2026: 1.

9 papers.

  1. 2024Improving Neural Biasing for Contextual Speech Recognition by Early Context Injection and Text Perturbationcited by 11
  2. 2025TurboBias: Universal ASR Context-Biasing powered by GPU-accelerated Phrase-Boosting Treecited by 4
  3. 2024Text Injection for Neural Contextual Biasingcited by 3
  4. 2025Evaluating ASR robustness to spontaneous speech errors: A study of WhisperX using a Speech Error Databasecited by 2
  5. 2024Self-consistent context aware conformer transducer for speech recognitioncited by 1
  6. 2024Unifying Global and Near-Context Biasing in a Single Trie Passcited by 1
  7. 2025PHRASED: Phrase Dictionary Biasing for Speech Translationcited by 1
  8. 2025A Neural Model for Contextual Biasing Score Learning and Filteringcited by 1
  9. 2026LOGIC: Efficient and Robust Contextual Biasing for Speech LLMs via Logit-Space Integrationcited by 0