LibriTTS-R：一個修復的多說話者文本轉語音語料庫

LibriTTS-R: A Restored Multi-Speaker Text-to-Speech Corpus

May 30, 2023

作者: Yuma Koizumi, Heiga Zen, Shigeki Karita, Yifan Ding, Kohei Yatabe, Nobuyuki Morioka, Michiel Bacchiani, Yu Zhang, Wei Han, Ankur Bapna

cs.AI

摘要

本文介紹了一個名為「LibriTTS-R」的新語音數據集，專為文本轉語音（TTS）而設計。它是通過將語音恢復應用於LibriTTS語料庫而衍生而來，該語料庫包含來自2,456位說話者的585小時24 kHz採樣率的語音數據以及相應的文本。LibriTTS-R的構成樣本與LibriTTS的樣本相同，只是聲音質量得到改善。實驗結果顯示，與LibriTTS中的樣本相比，LibriTTS-R的地面真實樣本的聲音質量顯著提高。此外，使用LibriTTS-R訓練的神經端到端TTS實現了與地面真實樣本相當的語音自然度。該語料庫可從http://www.openslr.org/141/免費下載。

English

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/.