Semi-Supervised Training for Improving Data Efficiency in End-to-End Speech Synthesis

Understand

Although end-to-end text-to-speech (TTS) models such as Tacotron have shown excellent results, they typically require a sizable set of high-quality <text, audio> pairs for training, which are expensive to collect.

• In this paper, we propose a semi-supervised training framework to improve the data efficiency of Tacotron.
• The idea is to allow Tacotron to utilize textual and acoustic knowledge contained in large, publicly-available text and speech corpora.
• Importantly, these external data are unpaired and potentially noisy.