Unsupervised Learning for Multi-Style Speech Synthesis with Limited Data

Abstract

Existing multi-style speech synthesis methods require either style labels or large amounts of unlabeled training data, making data acquisition difficult. In this paper, we present an unsupervised multi-style speech synthesis method that can be trained with limited data. We leverage instance discriminator to guide a style encoder to learn meaningful style representations from a multi-style dataset. Furthermore, we employ information bottleneck to filter out style-irrelevant information in the representations, which can improve speech quality and style similarity. Our method is able to produce desirable speech using a fairly small dataset, where the baseline GST-Tacotron fails. ABX tests show that our model significantly outperforms GST-Tacotron in both emotional speech synthesis task and multi-speaker speech synthesis task. In addition, we demonstrate that our method is able to learn meaningful style features with only 50 training samples per style.