# 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/en/paper/2406.06403v1/

## Key points

- Paper title: "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
- Challenge: Developing a text-to-speech synthesis system for over 7000 languages
- Combines massively multilingual pretraining with meta learning
- Approximates language representations to enable zero-shot speech synthesis
- Performance validated through objective measures and human evaluation
- Tested across diverse linguistic landscape
- Code and models made publicly available to empower communities with limited resources
- Potential implications for bridging communication gaps in linguistically diverse communities worldwide

## Summary

The paper "Meta Learning Text-to-Speech Synthesis in over 7000 Languages" by Florian Lux, Sarina Meyer, Lyonel Behringer, Frank Zalkow, Phat Do, Matt Coler, Emanuël A. P. Habets, and Ngoc Thang Vu addresses the challenge of developing a text-to-speech synthesis system capable of generating speech in more than 7000 languages. This is a difficult task due to the lack of sufficient data for traditional TTS development in many of these languages. To overcome this obstacle, the authors propose a novel approach that combines massively multilingual pretraining with meta learning to approximate language representations. This enables zero-shot speech synthesis in languages where no data is available. The performance of their system is validated through objective measures and human evaluation across a diverse linguistic landscape. By making their code and models publicly available, the authors hope to empower communities with limited linguistic resources and drive further advancements in speech technology. Accepted at Interspeech 2024, this groundbreaking research makes a significant contribution to the field of natural language processing and has potential implications for bridging communication gaps in linguistically diverse communities worldwide.