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arXiv · 2406.07096

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

Andrei Andrusenko·Aleksandr Laptev·Vladimir Bataev·Vitaly Lavrukhin·Boris Ginsburg
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Abstract

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.

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Andrei Andrusenko, Aleksandr Laptev, Vladimir Bataev, Vitaly Lavrukhin, Boris Ginsburg. 2024-06-11. Fast Context-Biasing for CTC and Transducer ASR models with CTC-based Word Spotter. https://arxiv.org/abs/2406.07096

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