# TurboBias: Universal ASR Context-Biasing powered by GPU-accelerated Phrase-Boosting Tree

arXiv: https://arxiv.org/abs/2508.07014 · HTML: https://mlsanity.com/paper/2508.07014/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 33 references that are in the map.
- First posted on arXiv 9 Aug 2025.

## Builds on (most cited first, 23 of 23)

- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [Listen, Attend and Spell](https://mlsanity.com/paper/1508.01211/) (2015)
- [Streaming End-to-end Speech Recognition For Mobile Devices](https://mlsanity.com/paper/1811.06621/) (2018)
- [Deep context: end-to-end contextual speech recognition](https://mlsanity.com/paper/1808.02480/) (2018)
- [End-to-End Speech Recognition: A Survey](https://mlsanity.com/paper/2303.03329/) (2023)
- [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)
- [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)
- [On the Comparison of Popular End-to-End Models for Large Scale Speech Recognition](https://mlsanity.com/paper/2005.14327/) (2020)
- [Earnings-21: A Practical Benchmark for ASR in the Wild](https://mlsanity.com/paper/2104.11348/) (2021)
- [Stateful Conformer with Cache-based Inference for Streaming Automatic Speech Recognition](https://mlsanity.com/paper/2312.17279/) (2023)
- [PromptASR for contextualized ASR with controllable style](https://mlsanity.com/paper/2309.07414/) (2023)
- [Speed of Light Exact Greedy Decoding for RNN-T Speech Recognition Models on GPU](https://mlsanity.com/paper/2406.03791/) (2024)
- [Improving Neural Biasing for Contextual Speech Recognition by Early Context Injection and Text Perturbation](https://mlsanity.com/paper/2407.10303/) (2024)
- [Spell my name: keyword boosted speech recognition](https://mlsanity.com/paper/2110.02791/) (2021)
- [Contextual Biasing with the Knuth-Morris-Pratt Matching Algorithm](https://mlsanity.com/paper/2310.00178/) (2023)
- [Label-Looping: Highly Efficient Decoding for Transducers](https://mlsanity.com/paper/2406.06220/) (2024)
- [Fast Context-Biasing for CTC and Transducer ASR models with CTC-based Word Spotter](https://mlsanity.com/paper/2406.07096/) (2024)
- [MultiMed: Multilingual Medical Speech Recognition via Attention Encoder Decoder](https://mlsanity.com/paper/2409.14074/) (2024)
- [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)

## Built on by (most cited first, 4 of 4)

- [Contextual Earnings-22: A Speech Recognition Benchmark with Custom Vocabulary in the Wild](https://mlsanity.com/paper/2604.07354/) (2026)
- [FlexCTC: GPU-powered CTC Beam Decoding With Advanced Contextual Abilities](https://mlsanity.com/paper/2508.07315/) (2025)
- [MLLP-VRAIN UPV system for the IWSLT 2026 Simultaneous Speech Translation task](https://mlsanity.com/paper/2606.17255/) (2026)
- [TurboBias 2.0: Streaming Context-Biasing for Production-Efficient ASR Systems](https://mlsanity.com/paper/2608.21343/) (2026)

## Abstract (arXiv)

Recognizing specific key phrases is an essential task for contextualized Automatic Speech Recognition (ASR). However, most existing context-biasing approaches have limitations associated with the necessity of additional model training, significantly slow down the decoding process, or constrain the choice of the ASR system type. This paper proposes a universal ASR context-biasing framework that supports all major types: CTC, Transducers, and Attention Encoder-Decoder models. The framework is based on a GPU-accelerated word boosting tree, which enables it to be used in shallow fusion mode for greedy and beam search decoding without noticeable speed degradation, even with a vast number of key phrases (up to 20K items). The obtained results showed high efficiency of the proposed method, surpassing the considered open-source context-biasing approaches in accuracy and decoding speed. Our context-biasing framework is open-sourced as a part of the NeMo toolkit.