# Varianceflow: High-Quality and Controllable Text-to-Speech using Variance Information via Normalizing Flow

arXiv: https://arxiv.org/abs/2302.13458 · HTML: https://mlsanity.com/paper/2302.13458/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2023.

## Built on by (most cited first, 3 of 3)

- [An Exhaustive Evaluation of TTS- and VC-based Data Augmentation for ASR](https://mlsanity.com/paper/2503.08954/) (2025)
- [Investigating Stochastic Methods for Prosody Modeling in Speech Synthesis](https://mlsanity.com/paper/2507.00227/) (2025)
- [MultiVerse: Efficient and Expressive Zero-Shot Multi-Task Text-to-Speech](https://mlsanity.com/paper/2410.03192/) (2024)

## Abstract (arXiv)

There are two types of methods for non-autoregressive text-to-speech models to learn the one-to-many relationship between text and speech effectively. The first one is to use an advanced generative framework such as normalizing flow (NF). The second one is to use variance information such as pitch or energy together when generating speech. For the second type, it is also possible to control the variance factors by adjusting the variance values provided to a model. In this paper, we propose a novel model called VarianceFlow combining the advantages of the two types. By modeling the variance with NF, VarianceFlow predicts the variance information more precisely with improved speech quality. Also, the objective function of NF makes the model use the variance information and the text in a disentangled manner resulting in more precise variance control. In experiments, VarianceFlow shows superior performance over other state-of-the-art TTS models both in terms of speech quality and controllability.