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CSTR VCTK Corpus

English Multi-speaker Corpus for CSTR Voice Cloning Toolkit

(Version 0.80)

RELEASE August 2012

The Centre for Speech Technology Research

University of Edinburgh

Copyright (c) 2012

Junichi Yamagishi

[email protected]

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Overview

This CSTR VCTK Corpus includes speech data uttered by 109 English

speakers with various accents. Each speaker reads out about 400

sentences, which were selected from a newspaper, the rainbow passage

and an elicitation paragraph used for the speech accent archive.

The newspaper texts were taken from Herald Glasgow, with permission

from Herald & Times Group. Each speaker has a different set of the

newspaper texts selected based a greedy algorithm that increases the

contextual and phonetic coverage.

The rainbow passage and elicitation paragraph are the same for all

speakers. The rainbow passage can be found at International Dialects

of English Archive:

(http://web.ku.edu/~idea/readings/rainbow.htm). The elicitation

paragraph is identical to the one used for the speech accent archive

(http://accent.gmu.edu). The details of the the speech accent archive

can be found at

http://www.ualberta.ca/~aacl2009/PDFs/WeinbergerKunath2009AACL.pdf

All speech data was recorded using an identical recording setup: an

omni-directional microphone (DPA 4035), 96kHz sampling frequency at 24

bits and in a hemi-anechoic chamber of the University of

Edinburgh. All recordings were converted into 16 bits, were downsampled

to 48 kHz based on STPK, and were manually end-pointed.

This corpus is aimed for HMM-based text-to-speech synthesis systems,

especially for speaker-adaptive HMM-based speech synthesis that uses

average voice models trained on multiple speakers and speaker

adaptation technologies.

COPYING

This corpus is licensed under Open Data Commons Attribution License

(ODC-By) v1.0.

http://opendatacommons.org/licenses/by/1.0/

http://opendatacommons.org/licenses/by/summary/

ACKNOWLEDGEMENTS

The CSTR VCTK Corpus was constructed by:

Christophe Veaux (University of Edinburgh)

Junichi Yamagishi (University of Edinburgh)

Kirsten MacDonald

The research leading to these results was partly funded from EPSRC

grants EP/I031022/1 (NST) and EP/J002526/1 (CAF), from the RSE-NSFC

grant (61111130120), and from the JST CREST (uDialogue).

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