It's challenging to customize transducer-based automatic speech recognition (ASR) system with context information which is dynamic and unavailable during model training. In this work, we introduce a light-weight contextual spelling correction model to correct context-related recognition errors in transducer-based ASR systems. We incorporate the context information into the spelling correction model with a shared context encoder and use a filtering algorithm to handle large-size context lists. Experiments show that the model improves baseline ASR model performance with about 50% relative word error rate reduction, which also significantly outperforms the baseline method such as contextual LM biasing. The model also shows excellent performance for out-of-vocabulary terms not seen during training.
@article{arxiv.2108.07493,
title = {A Light-weight contextual spelling correction model for customizing transducer-based speech recognition systems},
author = {Xiaoqiang Wang and Yanqing Liu and Sheng Zhao and Jinyu Li},
journal= {arXiv preprint arXiv:2108.07493},
year = {2021}
}