  Automatic Speech Recognition (ASR) based on Recurrent Neural Network
Transducers (RNN-T) is gaining interest in the speech community. We investigate
data selection and preparation choices aiming for improved robustness of RNN-T
ASR to speech disfluencies with a focus on partial words. For evaluation we use
clean data, data with disfluencies and a separate dataset with speech affected
by stuttering. We show that after including a small amount of data with
disfluencies in the training set the recognition accuracy on the tests with
disfluencies and stuttering improves. Increasing the amount of training data
with disfluencies gives additional gains without degradation on the clean data.
We also show that replacing partial words with a dedicated token helps to get
even better accuracy on utterances with disfluencies and stutter. The
evaluation of our best model shows 22.5% and 16.4% relative WER reduction on
those two evaluation sets.
