# Multi-Speaker End-to-End Speech Synthesis

arXiv: https://arxiv.org/abs/1907.04462 · HTML: https://mlsanity.com/paper/1907.04462/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2019.

## Built on by (most cited first, 2 of 2)

- [Investigating on Incorporating Pretrained and Learnable Speaker Representations for Multi-Speaker Multi-Style Text-to-Speech](https://mlsanity.com/paper/2103.04088/) (2021)
- [Combining speakers of multiple languages to improve quality of neural voices](https://mlsanity.com/paper/2108.07737/) (2021)

## Abstract (arXiv)

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.