Speech restoration (SR)  is a task of converting degraded speech signals into high-quality ones. In this study, we propose a robust SR model calledMiipher, and apply Miipher to a new SR application: increasing the amount of high-quality training data for speech generation by converting speech samples collected from the Web to studio-quality. To make our SR model robust against various degradation, we use (i)  a speech representation extracted from w2v-BERT for the input feature, and (ii)  a text representation extracted from transcripts via PnG-BERT as a linguistic conditioning feature. Experiments show that Miipher (i)  is robust against various audio degradation and (ii)  enable us to train a high-quality text-to-speech (TTS)  model from restored speech samples collected from the Web.
Audio samples are available at our demo page:google.github.io/df-conformer/miipher/.