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An unsupervised text-to-speech synthesis (TTS) system learns to generatespeech waveforms corresponding to any written sentence in a language byobserving: 1) a collection of untranscribed speech waveforms in that language;2) a collection of texts written in that language without access to anytranscribed speech. Developing such a system can significantly improve theavailability of speech technology to languages without a large amount ofparallel speech and text data. This paper proposes an unsupervised TTS systembased on an alignment module that outputs pseudo-text and another synthesismodule that uses pseudo-text for training and real text for inference. Ourunsupervised system can achieve comparable performance to the supervised systemin seven languages with about 10-20 hours of speech each. A careful study onthe effect of text units and vocoders has also been conducted to betterunderstand what factors may affect unsupervised TTS performance. The samplesgenerated by our models can be found athttps://cactuswiththoughts.github.io/UnsupTTS-Demo, and our code can be foundat https://github.com/lwang114/UnsupTTS.

Unsupervised Text-to-Speech Synthesis by Unsupervised Automatic Speech Recognition