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.

The detailed encoder architecture is depicted in Figure 7. We design the grouped 1x1 convolutions to be able to mix channels. Figure 8c shows an example. The decoder gets a mel-spectrogram and squeezes it. The, the decoder processes it through a number of flow blocks.

In this work, we propose Glow-TTS, a flow-based generative model for parallel TTS that does not require any external aligner.

Thanks all the reviewers for the detailed and thoughtful comments. HMM-based works [1, 2, 3], all of which proposed methods to estimate alignments from unsegmented data. We've not thoroughly explored to improve the duration predictor and simply follow the same We design the grouped 1x1 convolutions to be able to mix channels. For example, to generate a speech of 5.8 Therefore, adopting parallel TTS models significantly improves the sampling speed of end-to-end systems. In Section 5.3, we showed that varying temperature can change We will add a reference about Viterbi training.

The detailed encoder architecture is depicted in Figure 7. We design the grouped 1x1 convolutions to be able to mix channels. Figure 8c shows an example. The decoder gets a mel-spectrogram and squeezes it. The, the decoder processes it through a number of flow blocks.

In this work, we propose Glow-TTS, a flow-based generative model for parallel TTS that does not require any external aligner.

Thanks all the reviewers for the detailed and thoughtful comments. HMM-based works [1, 2, 3], all of which proposed methods to estimate alignments from unsegmented data. We've not thoroughly explored to improve the duration predictor and simply follow the same We design the grouped 1x1 convolutions to be able to mix channels. For example, to generate a speech of 5.8 Therefore, adopting parallel TTS models significantly improves the sampling speed of end-to-end systems. In Section 5.3, we showed that varying temperature can change We will add a reference about Viterbi training.

Weaknesses: I was a little confused about how the grouped 1x1 convolutions interact with the coupling layers. If the standard (half-and-half) partitioning is used for the coupling layers and the grouped 1x1 convolutions never mix channels outside of their group of 4, then half of the channels will never be transformed by any coupling layer. I'm assuming the authors deal with this issue somehow (since the results are good), but I only briefly scanned the code and didn't want to work through all of the index gymnastics. I could see readers being confused by these missing details. Update: In their response, the authors said they will explain more of the details of the grouped 1x1 convolutions in their revised version.

After rebuttal and discussion, all four reviewers provide very favorable reviews. The reviewers point out a novel methodology, combining flows with dynamic programming (hard monotonic alignment). The paper is therefore accepted for an oral.

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.