title: LibriTTS-R: A Restored Multi-Speaker Text-to-Speech Corpus
summary: This paper introduces a new speech dataset called ``LibriTTS-R'' designed for
  text-to-speech (TTS) use. It is derived by applying speech restoration to the
  LibriTTS corpus, which consists of 585 hours of speech data at 24 kHz sampling
  rate from 2,456 speakers and the corresponding texts. The constituent samples
  of LibriTTS-R are identical to those of LibriTTS, with only the sound quality
  improved. Experimental results show that the LibriTTS-R ground-truth samples
  showed significantly improved sound quality compared to those in LibriTTS. In
  addition, neural end-to-end TTS trained with LibriTTS-R achieved speech
  naturalness on par with that of the ground-truth samples. The corpus is freely
  available for download from http://www.openslr.org/141/.
paper_authors._id: 6476e29583d4fdaedde073d8
paper_authors.name: Yuma Koizumi
paper_authors._id: 6476e29583d4fdaedde073d9
paper_authors.name: Heiga Zen
paper_authors._id: 6476e29583d4fdaedde073da
paper_authors.name: Shigeki Karita
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-05-31T13:47:52.250Z
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paper_authors.avatarUrl: /avatars/15600b7e31a302baaee78e44e1d498d9.svg
paper_authors.fullname: Shigeki Karita
paper_authors.type: user
paper_authors.user: ShigekiKarita
paper_authors._id: 6476e29583d4fdaedde073db
paper_authors.name: Yifan Ding
paper_authors._id: 6476e29583d4fdaedde073dc
paper_authors.name: Kohei Yatabe
paper_authors._id: 6476e29583d4fdaedde073dd
paper_authors.name: Nobuyuki Morioka
paper_authors._id: 6476e29583d4fdaedde073de
paper_authors.name: Michiel Bacchiani
paper_authors._id: 6476e29583d4fdaedde073df
paper_authors.name: Yu Zhang
paper_authors._id: 6476e29583d4fdaedde073e0
paper_authors.name: Wei Han
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-06-23T14:28:00.962Z
paper_authors._id: 6489e3992f9e11a0c26973b6
paper_authors.avatarUrl: /avatars/e637e6caab6df56da6005f73c613a3d0.svg
paper_authors.fullname: Wei Han
paper_authors.type: user
paper_authors.user: weihan3
paper_authors._id: 6476e29583d4fdaedde073e1
paper_authors.name: Ankur Bapna
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-05-31T13:49:27.257Z
paper_authors._id: 62cdba279403ec4719d384f8
paper_authors.avatarUrl: /avatars/c62c7d16b5cf382cdeb7fbfb5e291917.svg
paper_authors.fullname: Ankur Bapna
paper_authors.type: user
paper_authors.user: ankurbpn
paper_title: LibriTTS-R: A Restored Multi-Speaker Text-to-Speech Corpus
paper_summary: This paper introduces a new speech dataset called ``LibriTTS-R'' designed for
  text-to-speech (TTS) use. It is derived by applying speech restoration to the
  LibriTTS corpus, which consists of 585 hours of speech data at 24 kHz sampling
  rate from 2,456 speakers and the corresponding texts. The constituent samples
  of LibriTTS-R are identical to those of LibriTTS, with only the sound quality
  improved. Experimental results show that the LibriTTS-R ground-truth samples
  showed significantly improved sound quality compared to those in LibriTTS. In
  addition, neural end-to-end TTS trained with LibriTTS-R achieved speech
  naturalness on par with that of the ground-truth samples. The corpus is freely
  available for download from http://www.openslr.org/141/.