  This paper presents a novel data augmentation technique for text-to-speech
(TTS), that allows to generate new (text, audio) training examples without
requiring any additional data. Our goal is to increase diversity of text
conditionings available during training. This helps to reduce overfitting,
especially in low-resource settings. Our method relies on substituting text and
audio fragments in a way that preserves syntactical correctness. We take
additional measures to ensure that synthesized speech does not contain
artifacts caused by combining inconsistent audio samples. The perceptual
evaluations show that our method improves speech quality over a number of
datasets, speakers, and TTS architectures. We also demonstrate that it greatly
improves robustness of attention-based TTS models.

Audio and Speech Processing;Computation and Language;Machine Learning