  In this paper, we construct a Japanese audiobook speech corpus called "J-MAC"
for speech synthesis research. With the success of reading-style speech
synthesis, the research target is shifting to tasks that use complicated
contexts. Audiobook speech synthesis is a good example that requires
cross-sentence, expressiveness, etc. Unlike reading-style speech,
speaker-specific expressiveness in audiobook speech also becomes the context.
To enhance this research, we propose a method of constructing a corpus from
audiobooks read by professional speakers. From many audiobooks and their texts,
our method can automatically extract and refine the data without any language
dependency. Specifically, we use vocal-instrumental separation to extract clean
data, connectionist temporal classification to roughly align text and audio,
and voice activity detection to refine the alignment. J-MAC is open-sourced in
our project page. We also conduct audiobook speech synthesis evaluations, and
the results give insights into audiobook speech synthesis.
