  Machine Speech Chain, which integrates both end-to-end (E2E) automatic speech
recognition (ASR) and text-to-speech (TTS) into one circle for joint training,
has been proven to be effective in data augmentation by leveraging large
amounts of unpaired data. In this paper, we explore the TTS->ASR pipeline in
speech chain to do domain adaptation for both neural TTS and E2E ASR models,
with only text data from target domain. We conduct experiments by adapting from
audiobook domain (LibriSpeech) to presentation domain (TED-LIUM), there is a
relative word error rate (WER) reduction of 10% for the E2E ASR model on the
TED-LIUM test set, and a relative WER reduction of 51.5% in synthetic speech
generated by neural TTS in the presentation domain. Further, we apply few-shot
speaker adaptation for the E2E ASR by using a few utterances from target
speakers in an unsupervised way, results in additional gains.
