Do not build your TTS training corpus randomly

TTS voice building generally relies on a script extracted from a big text corpus while optimizing the coverage of linguistic and phonological events supposedly related to voice acoustic quality. Previous works have shown differences on objective measures between smartly reduced and random corpora, but not when subjective evaluations are performed. For us, those results do not come from corpus reduction utility but from evaluations that smooth differences. In this article, we high-light those differences in a subjective test, by clustering test corpora according to a distance between signals so as to focus on different synthesized stimuli. The results show that covering appropriate features has a real impact on the perceived quality.