Multi-Speaker End-to-End Speech Synthesis
Computation and Language
2019-07-11 v1 Machine Learning
Sound
Audio and Speech Processing
Abstract
In this work, we extend ClariNet (Ping et al., 2019), a fully end-to-end speech synthesis model (i.e., text-to-wave), to generate high-fidelity speech from multiple speakers. To model the unique characteristic of different voices, low dimensional trainable speaker embeddings are shared across each component of ClariNet and trained together with the rest of the model. We demonstrate that the multi-speaker ClariNet outperforms state-of-the-art systems in terms of naturalness, because the whole model is jointly optimized in an end-to-end manner.
Cite
@article{arxiv.1907.04462,
title = {Multi-Speaker End-to-End Speech Synthesis},
author = {Jihyun Park and Kexin Zhao and Kainan Peng and Wei Ping},
journal= {arXiv preprint arXiv:1907.04462},
year = {2019}
}