An iterative active learning pipeline that filters web speech data by predicted quality and redundancy produces a TTS corpus with better speaker coverage at the same size.
JTubeSpeech: corpus of Japanese speech collected from YouTube for speech recognition and speaker verification
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abstract
In this paper, we construct a new Japanese speech corpus called "JTubeSpeech." Although recent end-to-end learning requires large-size speech corpora, open-sourced such corpora for languages other than English have not yet been established. In this paper, we describe the construction of a corpus from YouTube videos and subtitles for speech recognition and speaker verification. Our method can automatically filter the videos and subtitles with almost no language-dependent processes. We consistently employ Connectionist Temporal 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 1,300 hours of data and 2) 900 hours of data for Japanese ASV.
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Active Learning for Text-to-Speech Synthesis with Informative Sample Collection
An iterative active learning pipeline that filters web speech data by predicted quality and redundancy produces a TTS corpus with better speaker coverage at the same size.