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

## TL;DR

VarianceFlow is a novel text-to-speech model that combines normalizing flow with variance control, leading to more precise speech synthesis and better controllability compared to existing methods.

## Contribution

It introduces VarianceFlow, integrating normalizing flow with variance modeling to enhance speech quality and control in TTS systems.

## Key findings

- Outperforms state-of-the-art TTS models in speech quality
- Provides more accurate variance control for speech attributes
- Achieves superior controllability and naturalness in synthesized speech

## Abstract

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.