  This paper presents a new large-scale Japanese speech corpus for training
automatic speech recognition (ASR) systems. This corpus contains over 2,000
hours of speech with transcripts built on Japanese TV recordings and their
subtitles. We develop herein an iterative workflow to extract matching audio
and subtitle segments from TV recordings based on a conventional method for
lightly-supervised audio-to-text alignment. We evaluate a model trained with
our corpus using an evaluation dataset built on Japanese TEDx presentation
videos and confirm that the performance is better than that trained with the
Corpus of Spontaneous Japanese (CSJ). The experiment results show the
usefulness of our corpus for training ASR systems. This corpus is made public
for the research community along with Kaldi scripts for training the models
reported in this paper.
