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

Andrei Andrusenko; Vladimir Bataev; Lilit Grigoryan; Vitaly Lavrukhin; Boris Ginsburg

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arXiv · 2508.07014

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

Andrei Andrusenko·Vladimir Bataev·Lilit Grigoryan·Vitaly Lavrukhin·Boris Ginsburg

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Abstract

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.

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Andrei Andrusenko, Vladimir Bataev, Lilit Grigoryan, Vitaly Lavrukhin, Boris Ginsburg. 2025-08-09. TurboBias: Universal ASR Context-Biasing powered by GPU-accelerated Phrase-Boosting Tree. https://arxiv.org/abs/2508.07014

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