Transfer Learning Framework for Low-Resource Text-to-Speech using a Large-Scale Unlabeled Speech Corpus

Understand

Training a text-to-speech (TTS) model requires a large scale text labeled speech corpus, which is troublesome to collect.

• In this paper, we propose a transfer learning framework for TTS that utilizes a large amount of unlabeled speech dataset for pre-training.
• By leveraging wav2vec2.0 representation, unlabeled speech can highly improve performance, especially in the lack of labeled speech.
• We also extend the proposed method to zero-shot multi-speaker TTS (ZS-TTS).