  Contextual information plays a crucial role in speech recognition
technologies and incorporating it into the end-to-end speech recognition models
has drawn immense interest recently. However, previous deep bias methods lacked
explicit supervision for bias tasks. In this study, we introduce a contextual
phrase prediction network for an attention-based deep bias method. This network
predicts context phrases in utterances using contextual embeddings and
calculates bias loss to assist in the training of the contextualized model. Our
method achieved a significant word error rate (WER) reduction across various
end-to-end speech recognition models. Experiments on the LibriSpeech corpus
show that our proposed model obtains a 12.1% relative WER improvement over the
baseline model, and the WER of the context phrases decreases relatively by
40.5%. Moreover, by applying a context phrase filtering strategy, we also
effectively eliminate the WER degradation when using a larger biasing list.
