Construction of a Large-scale Japanese ASR Corpus on TV Recordings
This provides a valuable resource for researchers working on Japanese ASR, though it is incremental as it applies an existing alignment method to new data.
The authors tackled the problem of limited Japanese speech data for ASR by constructing a large-scale corpus of over 2,000 hours from TV recordings, and they showed that models trained on this corpus outperform those trained on the Corpus of Spontaneous Japanese on a TEDx evaluation dataset.
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.