mlsanityData to 14 Oct 2026

Lightweight Prompt Biasing for Contextualized End-to-End ASR Systems

arXiv 2506.06252 ↗First posted 6 Jun 2025eess.AS cs.CLcited by 1

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Abstract (arXiv)

End-to-End Automatic Speech Recognition (ASR) has advanced significantly yet still struggles with rare and domain-specific entities. This paper introduces a simple yet efficient prompt-based biasing technique for contextualized ASR, enhancing recognition accuracy by leverage a unified multitask learning framework. The approach comprises two key components: a prompt biasing model which is trained to determine when to focus on entities in prompt, and a entity filtering mechanism which efficiently filters out irrelevant entities. Our method significantly enhances ASR accuracy on entities, achieving a relative 30.7% and 18.0% reduction in Entity Word Error Rate compared to the baseline model with shallow fusion on in-house domain dataset with small and large entity lists, respectively. The primary advantage of this method lies in its efficiency and simplicity without any structure change, making it lightweight and highly efficient.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 25 of its 34 references that are in the map. First posted on arXiv 6 Jun 2025.

Builds on

25 of 34 references.

References · 25

  1. 2017Attention Is All You Needcited by 60,361
  2. 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 4,057
  3. 2020Conformer: Convolution-augmented Transformer for Speech Recognitioncited by 1,863
  4. 2015Listen, Attend and Spellcited by 543
  5. 2020Transformer Transducer: A Streamable Speech Recognition Model with Transformer Encoders and RNN-T Losscited by 235
  6. 2018Streaming End-to-end Speech Recognition For Mobile Devicescited by 234
  7. 2017State-of-the-art Speech Recognition With Sequence-to-Sequence Modelscited by 196
  8. 2021Recent Advances in End-to-End Automatic Speech Recognitioncited by 140
  9. 2018Deep context: end-to-end contextual speech recognitioncited by 137
  10. 2023End-to-End Speech Recognition: A Surveycited by 129
  11. 2021Contextualized Streaming End-to-End Speech Recognition with Trie-Based Deep Biasing and Shallow Fusioncited by 101
  12. 2020A Streaming On-Device End-to-End Model Surpassing Server-Side Conventional Model Quality and Latencycited by 100
13 more
  1. 2020Developing Real-time Streaming Transformer Transducer for Speech Recognition on Large-scale Datasetcited by 97
  2. 2020Contextual RNN-T For Open Domain ASRcited by 85
  3. 2020Deep Shallow Fusion for RNN-T Personalizationcited by 64
  4. 2020On the Comparison of Popular End-to-End Models for Large Scale Speech Recognitioncited by 63
  5. 2020Developing RNN-T Models Surpassing High-Performance Hybrid Models with Customization Capabilitycited by 63
  6. 2022Contextual Adapters for Personalized Speech Recognition in Neural Transducerscited by 57
  7. 2021Context-Aware Transformer Transducer for Speech Recognitioncited by 56
  8. 2021Tree-constrained Pointer Generator for End-to-end Contextual Speech Recognitioncited by 32
  9. 2023End-to-End Speech Recognition Contextualization with Large Language Modelscited by 22
  10. 2023Can Contextual Biasing Remain Effective with Whisper and GPT-2?cited by 21
  11. 2023PromptASR for contextualized ASR with controllable stylecited by 19
  12. 2024CTC-Assisted LLM-Based Contextual ASRcited by 19
  13. 2024Contextualized End-to-end Automatic Speech Recognition with Intermediate Biasing Losscited by 10

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026IndicContextEval: A Benchmark for Evaluating Context Utilisation in Audio Large Language Models Across 8 Indic Languagescited by 3