  Adaption of end-to-end speech recognition systems to new tasks is known to be
challenging. A number of solutions have been proposed which apply external
language models with various fusion methods, possibly with a combination of
two-pass decoding. Also TTS systems have been used to generate adaptation data
for the end-to-end models. In this paper we show that RNN-transducer models can
be effectively adapted to new domains using only small amounts of textual data.
By taking advantage of model's inherent structure, where the prediction network
is interpreted as a language model, we can apply fast adaptation to the model.
Adapting the model avoids the need for complicated decoding time fusions and
external language models. Using appropriate regularization, the prediction
network can be adapted to new domains while still retaining good generalization
capabilities. We show with multiple ASR evaluation tasks how this method can
provide relative gains of 10-45% in target task WER. We also share insights how
RNN-transducer prediction network performs as a language model.
