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

It's challenging to customize transducer-based automatic In this work, we propose a novel contextual biasing method speech recognition (ASR) system with context information which leverages contextual information by adding a contextual which is dynamic and unavailable during model training. In spelling correction (CSC) model on top of the transducer this work, we introduce a light-weight contextual spelling correction model. To consider contextual information during correction, model to correct context-related recognition errors in a context encoder which encodes context phrases into hidden transducer-based ASR systems. We incorporate the context information embeddings is added to the spelling correction model [16, 17], into the spelling correction model with a shared context the decoder of the correction model then attends to the context encoder and use a filtering algorithm to handle large-size encoder and text encoder by attention mechanism [18].