  Personal rare word recognition in end-to-end Automatic Speech Recognition
(E2E ASR) models is a challenge due to the lack of training data. A standard
way to address this issue is with shallow fusion methods at inference time.
However, due to their dependence on external language models and the
deterministic approach to weight boosting, their performance is limited. In
this paper, we propose training neural contextual adapters for personalization
in neural transducer based ASR models. Our approach can not only bias towards
user-defined words, but also has the flexibility to work with pretrained ASR
models. Using an in-house dataset, we demonstrate that contextual adapters can
be applied to any general purpose pretrained ASR model to improve
personalization. Our method outperforms shallow fusion, while retaining
functionality of the pretrained models by not altering any of the model
weights. We further show that the adapter style training is superior to
full-fine-tuning of the ASR models on datasets with user-defined content.
