  In this paper, several works are proposed to address practical challenges for
deploying RNN Transducer (RNN-T) based speech recognition system. These
challenges are adapting a well-trained RNN-T model to a new domain without
collecting the audio data, obtaining time stamps and confidence scores at word
level. The first challenge is solved with a splicing data method which
concatenates the speech segments extracted from the source domain data. To get
the time stamp, a phone prediction branch is added to the RNN-T model by
sharing the encoder for the purpose of force alignment. Finally, we obtain
word-level confidence scores by utilizing several types of features calculated
during decoding and from confusion network. Evaluated with Microsoft production
data, the splicing data adaptation method improves the baseline and adaptation
with the text to speech method by 58.03% and 15.25% relative word error rate
reduction, respectively. The proposed time stamping method can get less than
50ms word timing difference from the ground truth alignment on average while
maintaining the recognition accuracy of the RNN-T model. We also obtain high
confidence annotation performance with limited computation cost.
