  In this paper, we explore the benefits of incorporating context into a
Recurrent Neural Network (RNN-T) based Automatic Speech Recognition (ASR) model
to improve the speech recognition for virtual assistants. Specifically, we use
meta information extracted from the time at which the utterance is spoken and
the approximate location information to make ASR context aware. We show that
these contextual information, when used individually, improves overall
performance by as much as 3.48% relative to the baseline and when the contexts
are combined, the model learns complementary features and the recognition
improves by 4.62%. On specific domains, these contextual signals show
improvements as high as 11.5%, without any significant degradation on others.
We ran experiments with models trained on data of sizes 30K hours and 10K
hours. We show that the scale of improvement with the 10K hours dataset is much
higher than the one obtained with 30K hours dataset. Our results indicate that
with limited data to train the ASR model, contextual signals can improve the
performance significantly.
