Distribution augmentation for low-resource expressive text-to-speech

• URL: http://arxiv.org/abs/2202.06409v1
• Date: Sun, 13 Feb 2022 21:19:31 GMT
• Title: Distribution augmentation for low-resource expressive text-to-speech
• Authors: Mateusz Lajszczak, Animesh Prasad, Arent van Korlaar, Bajibabu Bollepalli, Antonio Bonafonte, Arnaud Joly, Marco Nicolis, Alexis Moinet, Thomas Drugman, Trevor Wood, Elena Sokolova
• Abstract summary: This paper presents a novel data augmentation technique for text-to-speech (TTS) It allows to generate new (text, audio) training examples without requiring any additional data.
• Score: 18.553812159109253
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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