# FastSpeech: Fast, Robust and Controllable Text to Speech

**Authors:** Yi Ren, Yangjun Ruan, Xu Tan, Tao Qin, Sheng Zhao, Zhou Zhao, Tie-Yan Liu  
**arXiv:** [1905.09263](https://arxiv.org/abs/1905.09263)  
**Date:** 2019-05-22  
**Summary page:** https://www.summarizepaper.com/en/paper/1905.09263v1/

## Key points

- Novel approach to text-to-speech synthesis using a feed-forward network based on Transformer architecture
- Traditional neural network based end-to-end model for TTS with slow inference speed and issues with robustness and controllability
- Dataset used for experiments to demonstrate the effectiveness of FastSpeech in parallel mel-spectrogram generation
- Models that generate speech by predicting one audio sample at a time, resulting in slower inference speed compared to parallel models
- FastSpeech's remarkable speed improvement makes it a promising solution for applications requiring fast and controllable text-to-speech synthesis

## Summary

: A novel approach to text-to-speech synthesis using a feed-forward network based on Transformer architecture. : Traditional neural network based end-to-end model for TTS with slow inference speed and issues with robustness and controllability. : Dataset used for experiments to demonstrate the effectiveness of FastSpeech in parallel mel-spectrogram generation. : Models that generate speech by predicting one audio sample at a time, resulting in slower inference speed compared to parallel models. : FastSpeech's remarkable speed improvement makes it a promising solution for applications requiring fast and controllable text-to-speech synthesis.