# Fast Context-Biasing for CTC and Transducer ASR models with CTC-based Word Spotter

arXiv: https://arxiv.org/abs/2406.07096 · HTML: https://mlsanity.com/paper/2406.07096/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 27 references that are in the map.
- First posted on arXiv 11 Jun 2024.

## Builds on (most cited first, 18 of 18)

- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [BPE-Dropout: Simple and Effective Subword Regularization](https://mlsanity.com/paper/1910.13267/) (2019)
- [Deep context: end-to-end contextual speech recognition](https://mlsanity.com/paper/1808.02480/) (2018)
- [Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition](https://mlsanity.com/paper/2305.05084/) (2023)
- [Contextualized Streaming End-to-End Speech Recognition with Trie-Based Deep Biasing and Shallow Fusion](https://mlsanity.com/paper/2104.02194/) (2021)
- [Contextual RNN-T For Open Domain ASR](https://mlsanity.com/paper/2006.03411/) (2020)
- [CTC-Segmentation of Large Corpora for German End-to-end Speech Recognition](https://mlsanity.com/paper/2007.09127/) (2020)
- [SALM: Speech-augmented Language Model with In-context Learning for Speech Recognition and Translation](https://mlsanity.com/paper/2310.09424/) (2023)
- [SLM: Bridge the thin gap between speech and text foundation models](https://mlsanity.com/paper/2310.00230/) (2023)
- [NeMo Inverse Text Normalization: From Development To Production](https://mlsanity.com/paper/2104.05055/) (2021)
- [Stateful Conformer with Cache-based Inference for Streaming Automatic Speech Recognition](https://mlsanity.com/paper/2312.17279/) (2023)
- [Tiny Transducer: A Highly-efficient Speech Recognition Model on Edge Devices](https://mlsanity.com/paper/2101.06856/) (2021)
- [PromptASR for contextualized ASR with controllable style](https://mlsanity.com/paper/2309.07414/) (2023)
- [Improving Contextual Recognition of Rare Words with an Alternate Spelling Prediction Model](https://mlsanity.com/paper/2209.01250/) (2022)
- [Accelerating RNN Transducer Inference via One-Step Constrained Beam Search](https://mlsanity.com/paper/2002.03577/) (2020)
- [Spell my name: keyword boosted speech recognition](https://mlsanity.com/paper/2110.02791/) (2021)
- [GPU-Accelerated WFST Beam Search Decoder for CTC-based Speech Recognition](https://mlsanity.com/paper/2311.04996/) (2023)
- [Dynamic Acoustic Unit Augmentation With BPE-Dropout for Low-Resource End-to-End Speech Recognition](https://mlsanity.com/paper/2103.07186/) (2021)

## Built on by (most cited first, 7 of 7)

- [NGPU-LM: GPU-Accelerated N-Gram Language Model for Context-Biasing in Greedy ASR Decoding](https://mlsanity.com/paper/2505.22857/) (2025)
- [Pushing the Limits of Beam Search Decoding for Transducer-based ASR models](https://mlsanity.com/paper/2506.00185/) (2025)
- [TurboBias: Universal ASR Context-Biasing powered by GPU-accelerated Phrase-Boosting Tree](https://mlsanity.com/paper/2508.07014/) (2025)
- [Contextual Earnings-22: A Speech Recognition Benchmark with Custom Vocabulary in the Wild](https://mlsanity.com/paper/2604.07354/) (2026)
- [TurboBias 2.0: Streaming Context-Biasing for Production-Efficient ASR Systems](https://mlsanity.com/paper/2608.21343/) (2026)
- [PTC-Bias: Phoneme-Level Temporal Competition for Bias Retrieval and Post-Decoding Correction in Speech LLMs](https://mlsanity.com/paper/2609.28727/) (2026)
- [BaLEEN: Biasing with Latent Encoded Entities for Context-Aware ASR](https://mlsanity.com/paper/2609.36913/) (2026)

## Abstract (arXiv)

Accurate recognition of rare and new words remains a pressing problem for contextualized Automatic Speech Recognition (ASR) systems. Most context-biasing methods involve modification of the ASR model or the beam-search decoding algorithm, complicating model reuse and slowing down inference. This work presents a new approach to fast context-biasing with CTC-based Word Spotter (CTC-WS) for CTC and Transducer (RNN-T) ASR models. The proposed method matches CTC log-probabilities against a compact context graph to detect potential context-biasing candidates. The valid candidates then replace their greedy recognition counterparts in corresponding frame intervals. A Hybrid Transducer-CTC model enables the CTC-WS application for the Transducer model. The results demonstrate a significant acceleration of the context-biasing recognition with a simultaneous improvement in F-score and WER compared to baseline methods. The proposed method is publicly available in the NVIDIA NeMo toolkit.