  Automatic Speech Recognition(ASR) has been dominated by deep learning-based
end-to-end speech recognition models. These approaches require large amounts of
labeled data in the form of audio-text pairs. Moreover, these models are more
susceptible to domain shift as compared to traditional models. It is common
practice to train generic ASR models and then adapt them to target domains
using comparatively smaller data sets. We consider a more extreme case of
domain adaptation where text-only corpus is available. In this work, we propose
a simple baseline technique for domain adaptation in end-to-end speech
recognition models. We convert the text-only corpus to audio data using single
speaker Text to Speech (TTS) engine. The parallel data in the target domain is
then used to fine-tune the final dense layer of generic ASR models. We show
that single speaker synthetic TTS data coupled with final dense layer only
fine-tuning provides reasonable improvements in word error rates. We use text
data from address and e-commerce search domains to show the effectiveness of
our low-cost baseline approach on CTC and attention-based models.
