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Contextualized Streaming End-to-End Speech Recognition with Trie-Based Deep Biasing and Shallow Fusion

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arxiv 2104.02194 v2 pith:VXWM3Q2G submitted 2021-04-05 cs.CL cs.LGeess.AS

Contextualized Streaming End-to-End Speech Recognition with Trie-Based Deep Biasing and Shallow Fusion

Duc Le , Mahaveer Jain , Gil Keren , Suyoun Kim , Yangyang Shi , Jay Mahadeokar , Julian Chan , Yuan Shangguan
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Christian Fuegen Ozlem Kalinli Yatharth Saraf Michael L. Seltzer
This is my paper
classification cs.CL cs.LGeess.AS
keywords biasingcontextualcontextualizationdeepend-to-endfusionimprovementlong-tail
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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How to leverage dynamic contextual information in end-to-end speech recognition has remained an active research area. Previous solutions to this problem were either designed for specialized use cases that did not generalize well to open-domain scenarios, did not scale to large biasing lists, or underperformed on rare long-tail words. We address these limitations by proposing a novel solution that combines shallow fusion, trie-based deep biasing, and neural network language model contextualization. These techniques result in significant 19.5% relative Word Error Rate improvement over existing contextual biasing approaches and 5.4%-9.3% improvement compared to a strong hybrid baseline on both open-domain and constrained contextualization tasks, where the targets consist of mostly rare long-tail words. Our final system remains lightweight and modular, allowing for quick modification without model re-training.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Improving Synthetic Data Training for Contextual Biasing Models with a Keyword-Aware Cost Function

    cs.CL 2025-09 conditional novelty 6.0

    A keyword-aware loss with masked cross-entropy and binary gating terms reduces overfitting in synthetic-data training of TCPGen, improving Whisper WER on NSC Part 2 from 14.16% (AGEM baseline) to 11.81%.

  2. Attention2Probability: Attention-Driven Terminology Probability Estimation for Robust Speech-to-Text System

    cs.CL 2025-08 conditional novelty 6.0

    A cross-attention term retriever estimates which terminology appears in speech and, when its top-k terms are added to the prompt, improves SLM terminology accuracy by 6-17%.

  3. Efficient Trie-based Biasing using K-step Prediction for Rare Word Recognition

    cs.CL 2025-09 conditional novelty 5.0

    A future-token prediction branch in Whisper gates trie-based biasing rewards, letting greedy decoding recognize rare words without a beam-search reward revocation step.

  4. Non-Intrusive Automatic Speech Recognition Refinement: A Survey

    eess.AS 2025-08 accept novelty 4.0

    A survey that classifies non-intrusive ASR refinement methods into five categories, reviews domain adaptation and evaluation datasets, proposes standardized metrics, and identifies future research directions.