  Transfer tasks in text-to-speech (TTS) synthesis - where one or more aspects
of the speech of one set of speakers is transferred to another set of speakers
that do not feature these aspects originally - remains a challenging task. One
of the challenges is that models that have high-quality transfer capabilities
can have issues in stability, making them impractical for user-facing critical
tasks. This paper demonstrates that transfer can be obtained by training a
robust TTS system on data generated by a less robust TTS system designed for a
high-quality transfer task; in particular, a CHiVE-BERT monolingual TTS system
is trained on the output of a Tacotron model designed for accent transfer.
While some quality loss is inevitable with this approach, experimental results
show that the models trained on synthetic data this way can produce high
quality audio displaying accent transfer, while preserving speaker
characteristics such as speaking style.
