  End-to-end (E2E) systems for automatic speech recognition (ASR), such as RNN
Transducer (RNN-T) and Listen-Attend-Spell (LAS) blend the individual
components of a traditional hybrid ASR system - acoustic model, language model,
pronunciation model - into a single neural network. While this has some nice
advantages, it limits the system to be trained using only paired audio and
text. Because of this, E2E models tend to have difficulties with correctly
recognizing rare words that are not frequently seen during training, such as
entity names. In this paper, we propose modifications to the RNN-T model that
allow the model to utilize additional metadata text with the objective of
improving performance on these named entity words. We evaluate our approach on
an in-house dataset sampled from de-identified public social media videos,
which represent an open domain ASR task. By using an attention model and a
biasing model to leverage the contextual metadata that accompanies a video, we
observe a relative improvement of about 16% in Word Error Rate on Named
Entities (WER-NE) for videos with related metadata.
