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

Training Text-To-Speech Systems From Synthetic Data: A Practical Approach For Accent Transfer Tasks