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