  Training of multi-speaker text-to-speech (TTS) systems relies on curated
datasets based on high-quality recordings or audiobooks. Such datasets often
lack speaker diversity and are expensive to collect. As an alternative, recent
studies have leveraged the availability of large, crowdsourced automatic speech
recognition (ASR) datasets. A major problem with such datasets is the presence
of noisy and/or distorted samples, which degrade TTS quality. In this paper, we
propose to automatically select high-quality training samples using a
non-intrusive mean opinion score (MOS) estimator, WV-MOS. We show the viability
of this approach for training a multi-speaker GlowTTS model on the Common Voice
English dataset. Our approach improves the overall quality of generated
utterances by 1.26 MOS point with respect to training on all the samples and by
0.35 MOS point with respect to training on the LibriTTS dataset. This opens the
door to automatic TTS dataset curation for a wider range of languages.
