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

Created by
  • Haebom

Author

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

Outline

This paper addresses the problem of recognizing specific keywords in context-aware automatic speech recognition (ASR). Existing context-biased techniques have limitations, such as requiring additional model training, slow decoding speed, and limited ASR system types. In this paper, we propose a general-purpose ASR context-biased framework that supports all major ASR model types, including CTC, Transducer, and Attention Encoder-Decoder models. Using GPU-accelerated word boosting trees, the framework operates in shallow fusion mode without slowdown in greedy and beam search decoding, even with up to 20,000 keywords. Experimental results demonstrate that the proposed method outperforms existing open-source context-biased techniques in terms of accuracy and decoding speed. The proposed context-biased framework has been open-sourced as part of the NeMo toolkit.

Takeaways, Limitations

Takeaways:
We present a general context-biased framework applicable to various ASR model types.
GPU acceleration allows processing of large numbers of keywords without slowdown
Achieving higher accuracy and decoding speed compared to existing methods
Open source release for improved accessibility
Limitations:
The performance of the framework presented in this paper is based on a specific dataset and keyword set, and performance may vary under other conditions.
It can be applied to various ASR models, but there may be room for optimization depending on the model type.
There is insufficient consideration for the use of contextual information other than keywords.
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