LibriTTS-R: A Restored Multi-Speaker Text-to-Speech Corpus
Authors: name: Yuma Koizumi; name: Heiga Zen; name: Shigeki Karita; name: Yifan Ding; name: Kohei Yatabe; name: Nobuyuki Morioka; name: Michiel Bacchiani; name: Yu Zhang; name: Wei Han; name: Ankur Bapna
Published: 2023-05-30 07:30:21

Abstract: 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/.