  Thanks to improvements in machine learning techniques, including deep
learning, speech synthesis is becoming a machine learning task. To accelerate
speech synthesis research, we are developing Japanese voice corpora reasonably
accessible from not only academic institutions but also commercial companies.
In 2017, we released the JSUT corpus, which contains 10 hours of reading-style
speech uttered by a single speaker, for end-to-end text-to-speech synthesis.
For more general use in speech synthesis research, e.g., voice conversion and
multi-speaker modeling, in this paper, we construct the JVS corpus, which
contains voice data of 100 speakers in three styles (normal, whisper, and
falsetto). The corpus contains 30 hours of voice data including 22 hours of
parallel normal voices. This paper describes how we designed the corpus and
summarizes the specifications. The corpus is available at our project page.
