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In this paper, we construct a new Japanese speech corpus called"JTubeSpeech." Although recent end-to-end learning requires large-size speechcorpora, open-sourced such corpora for languages other than English have notyet been established. In this paper, we describe the construction of a corpusfrom YouTube videos and subtitles for speech recognition and speakerverification. Our method can automatically filter the videos and subtitles withalmost no language-dependent processes. We consistently employ ConnectionistTemporal Classification (CTC)-based techniques for automatic speech recognition(ASR) and a speaker variation-based method for automatic speaker verification(ASV). We build 1) a large-scale Japanese ASR benchmark with more than 1,300hours of data and 2) 900 hours of data for Japanese ASV.

JTubeSpeech: corpus of Japanese speech collected from YouTube for speech recognition and speaker verification