Discrete Acoustic Space for an Efficient Sampling in Neural Text-To-Speech
Abstract
We present a Split Vector Quantized Variational Autoencoder (SVQ-VAE) architecture using a split vector quantizer for NTTS, as an enhancement to the well-known Variational Autoencoder (VAE) and Vector Quantized Variational Autoencoder (VQ-VAE) architectures. Compared to these previous architectures, our proposed model retains the benefits of using an utterance-level bottleneck, while keeping significant representation power and a discretized latent space small enough for efficient prediction from text. We train the model on recordings in the expressive task-oriented dialogues domain and show that SVQ-VAE achieves a statistically significant improvement in naturalness over the VAE and VQ-VAE models. Furthermore, we demonstrate that the SVQ-VAE latent acoustic space is predictable from text, reducing the gap between the standard constant vector synthesis and vocoded recordings by 32%.
Cite
@article{arxiv.2110.12539,
title = {Discrete Acoustic Space for an Efficient Sampling in Neural Text-To-Speech},
author = {Marek Strong and Jonas Rohnke and Antonio Bonafonte and Mateusz Łajszczak and Trevor Wood},
journal= {arXiv preprint arXiv:2110.12539},
year = {2023}
}
Comments
5 pages, 5 figures, accepted at IberSPEECH 2022