  State-of-the-art text-to-speech (TTS) systems require several hours of
recorded speech data to generate high-quality synthetic speech. When using
reduced amounts of training data, standard TTS models suffer from speech
quality and intelligibility degradations, making training low-resource TTS
systems problematic. In this paper, we propose a novel extremely low-resource
TTS method called Voice Filter that uses as little as one minute of speech from
a target speaker. It uses voice conversion (VC) as a post-processing module
appended to a pre-existing high-quality TTS system and marks a conceptual shift
in the existing TTS paradigm, framing the few-shot TTS problem as a VC task.
Furthermore, we propose to use a duration-controllable TTS system to create a
parallel speech corpus to facilitate the VC task. Results show that the Voice
Filter outperforms state-of-the-art few-shot speech synthesis techniques in
terms of objective and subjective metrics on one minute of speech on a diverse
set of voices, while being competitive against a TTS model built on 30 times
more data.
