  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.
