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.

通过 CTC-based Word Spotter 实现快速的上下文偏置识别，加速上下文偏置识别的同时提高了 F-score 和 WER，方法已经在 NVIDIA NeMo toolkit 中提供。