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Structure Before Sampling: Community-Aware Core-Set Selection for Data-Efficient Text-to-Speech

arXiv · AI, language, vision and robotics · article · Sep 21, 2026 · UTC

Text-to-speech (TTS) corpora are costly to record, yet many utterances add little new phonetic information. Core-set selection reduces this cost by choosing a small training subset under a fixed audio-duration budget. We represent a corpus as a phonotactic graph that links each utterance to its most phonemically similar ones, and we first test whether this graph has structure. In Bangla and English corpora, its clustering is 199 and 56 times that of a size-matched random graph, and its modularity is more than twice that of a degree-preserving random graph. We then propose Community Representat

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