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

• URL: http://arxiv.org/abs/2508.07014v2
• Date: Tue, 12 Aug 2025 14:25:57 GMT
• Title: TurboBias: Universal ASR Context-Biasing powered by GPU-accelerated Phrase-Boosting Tree
• Authors: Andrei Andrusenko, Vladimir Bataev, Lilit Grigoryan, Vitaly Lavrukhin, Boris Ginsburg,
• Abstract summary: This paper proposes a universal context-biasing framework for Automatic Speech Recognition (ASR)<n>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.<n>The obtained results showed high efficiency of the proposed method, surpassing the considered open-source context-biasing approaches.
• Score: 17.16475665648591
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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