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• W4386764866 abstract "Speech restoration (SR) is a task of converting degraded speech signals into high-quality ones. In this study, we propose a robust SR model called Miipher, 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/." @default.
• W4386764866 created "2023-09-16" @default.
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• W4386764866 date "2023-10-22" @default.
• W4386764866 modified "2023-09-27" @default.
• W4386764866 title "Miipher: A Robust Speech Restoration Model Integrating Self-Supervised Speech and Text Representations" @default.
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• W4386764866 doi "https://doi.org/10.1109/waspaa58266.2023.10248089" @default.
• W4386764866 hasPublicationYear "2023" @default.
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