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This paper presents a new large-scale Japanese speech corpus for trainingautomatic speech recognition (ASR) systems. This corpus contains over 2,000hours of speech with transcripts built on Japanese TV recordings and theirsubtitles. We develop herein an iterative workflow to extract matching audioand subtitle segments from TV recordings based on a conventional method forlightly-supervised audio-to-text alignment. We evaluate a model trained withour corpus using an evaluation dataset built on Japanese TEDx presentationvideos and confirm that the performance is better than that trained with theCorpus of Spontaneous Japanese (CSJ). The experiment results show theusefulness of our corpus for training ASR systems. This corpus is made publicfor the research community along with Kaldi scripts for training the modelsreported in this paper.

Construction of a Large-scale Japanese ASR Corpus on TV Recordings