A Light-weight contextual spelling correction model for customizing transducer-based speech recognition systems

• URL: http://arxiv.org/abs/2108.07493v1
• Date: Tue, 17 Aug 2021 08:14:37 GMT
• Title: A Light-weight contextual spelling correction model for customizing transducer-based speech recognition systems
• Authors: Xiaoqiang Wang, Yanqing Liu, Sheng Zhao, Jinyu Li
• Abstract summary: We introduce a light-weight contextual spelling correction model to correct context-related recognition errors. Experiments show that the model improves baseline ASR model performance with about 50% relative word error rate reduction. The model also shows excellent performance for out-of-vocabulary terms not seen during training.
• Score: 42.05399301143457
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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