Glow-TTS: A Generative Flow for Text-to-Speech via Monotonic Alignment Search

TLDR

Recent parallel TTS models such as FastSpeech and ParaNet generate mel‑spectrograms from text, but they rely on autoregressive models as external aligners. The authors introduce Glow‑TTS, a flow‑based generative model for parallel TTS that eliminates the need for any external aligner. Glow‑TTS combines flow modeling with dynamic programming to autonomously search for the most probable monotonic alignment between text and the speech latent representation. The model delivers robust, fast, and controllable synthesis that generalizes to long utterances, achieves an order‑of‑magnitude speed‑up over Tacotron 2 with comparable quality, and can be readily extended to multi‑speaker settings.

Abstract

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.