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State-of-the-art text-to-speech (TTS) systems require several hours ofrecorded speech data to generate high-quality synthetic speech. When usingreduced amounts of training data, standard TTS models suffer from speechquality and intelligibility degradations, making training low-resource TTSsystems problematic. In this paper, we propose a novel extremely low-resourceTTS method called Voice Filter that uses as little as one minute of speech froma target speaker. It uses voice conversion (VC) as a post-processing moduleappended to a pre-existing high-quality TTS system and marks a conceptual shiftin 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 aparallel speech corpus to facilitate the VC task. Results show that the VoiceFilter outperforms state-of-the-art few-shot speech synthesis techniques interms of objective and subjective metrics on one minute of speech on a diverseset of voices, while being competitive against a TTS model built on 30 timesmore data.

Voice Filter: Few-shot text-to-speech speaker adaptation using voice conversion as a post-processing module