Paper Info
Title
A Light-Weight Contextual Spelling Correction Model for Customizing Transducer-Based Speech Recognition Systems.
Abstract
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.
Year
DOI
Venue
2021
10.21437/Interspeech.2021-379
Interspeech
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Xiaoqiang Wang101.35
Yanqing Liu233.10
Sheng Zhao3249.16
Jinyu Li422.39