# Meta Learning Text-to-Speech Synthesis in over 7000 Languages

**Authors:** Florian Lux, Sarina Meyer, Lyonel Behringer, Frank Zalkow, Phat Do, Matt Coler, Emanuël A. P. Habets, Ngoc Thang Vu  
**arXiv:** [2406.06403](https://arxiv.org/abs/2406.06403)  
**Date:** 2024-06-10  
**Summary page:** https://www.summarizepaper.com/fr/paper/2406.06403v1/

## Abstract

In this work, we take on the challenging task of building a single text-to-speech synthesis system that is capable of generating speech in over 7000 languages, many of which lack sufficient data for traditional TTS development. By leveraging a novel integration of massively multilingual pretraining and meta learning to approximate language representations, our approach enables zero-shot speech synthesis in languages without any available data. We validate our system's performance through objective measures and human evaluation across a diverse linguistic landscape. By releasing our code and models publicly, we aim to empower communities with limited linguistic resources and foster further innovation in the field of speech technology.