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, publiclyavailable text and speech corpora. Importantly, these external data are unpaired and potentially noisy. Specifically, first we embed each word in the input text into word vectors and condition the Tacotron encoder on them. We then use an unpaired speech corpus to pre-train the Tacotron decoder in the acoustic domain. Finally, we fine-tune the model using available paired data. We demonstrate that the proposed framework enables Tacotron to generate intelligible speech using less than half an hour of paired training data. Index Terms-Tacotron, text-to-speech, semi-supervised learning, pre-training, data efficiency * Work done while at Google. Sound demos can be found at https://google.github.io/ tacotron/publications/semisupervised.
Chung et al. proposed a data efficient semi-supervised training of TTS model that trains the model using a small amount of text-speech pair data with help of decoder pretraining REF .
Semi-supervised Training for Improving Data Efficiency in End-to-end Speech Synthesis
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Computer Science Engineering