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This paper proposes a method for selecting training data for text-to-speech(TTS) synthesis from dark data. TTS models are typically trained onhigh-quality speech corpora that cost much time and money for data collection,which makes it very challenging to increase speaker variation. In contrast,there is a large amount of data whose availability is unknown (a.k.a, "darkdata"), such as YouTube videos. To utilize data other than TTS corpora,previous studies have selected speech data from the corpora on the basis ofacoustic quality. However, considering that TTS models robust to data noisehave been proposed, we should select data on the basis of its importance astraining data to the given TTS model, not the quality of speech itself. Ourmethod with a loop of training and evaluation selects training data on thebasis of the automatically predicted quality of synthetic speech of a given TTSmodel. Results of evaluations using YouTube data reveal that our methodoutperforms the conventional acoustic-quality-based method.

Text-to-speech synthesis from dark data with evaluation-in-the-loop data selection