  Recently, text-to-speech (TTS) models such as FastSpeech and ParaNet have
been proposed to generate mel-spectrograms from text in parallel. Despite the
advantage, the parallel TTS models cannot be trained without guidance from
autoregressive TTS models as their external aligners. In this work, we propose
Glow-TTS, a flow-based generative model for parallel TTS that does not require
any external aligner. By combining the properties of flows and dynamic
programming, the proposed model searches for the most probable monotonic
alignment between text and the latent representation of speech on its own. We
demonstrate that enforcing hard monotonic alignments enables robust TTS, which
generalizes to long utterances, and employing generative flows enables fast,
diverse, and controllable speech synthesis. Glow-TTS obtains an
order-of-magnitude speed-up over the autoregressive model, Tacotron 2, at
synthesis with comparable speech quality. We further show that our model can be
easily extended to a multi-speaker setting.
