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This paper proposes a method for extracting a lightweight subset from atext-to-speech (TTS) corpus ensuring synthetic speech quality. In recent years,methods have been proposed for constructing large-scale TTS corpora bycollecting diverse data from massive sources such as audiobooks and YouTube.Although these methods have gained significant attention for enhancing theexpressive capabilities of TTS systems, they often prioritize collecting vastamounts of data without considering practical constraints like storage capacityand computation time in training, which limits the available data quantity.Consequently, the need arises to efficiently collect data within these volumeconstraints. To address this, we propose a method for selecting the coresubset~(known as \textit{core-set}) from a TTS corpus on the basis of a\textit{diversity metric}, which measures the degree to which a subsetencompasses a wide range. Experimental results demonstrate that our proposedmethod performs significantly better than the baseline phoneme-balanced dataselection across language and corpus size.

Diversity-based core-set selection for text-to-speech with linguistic and acoustic features