Prominent methods (e.g., Tacotron 2)usuallyfirst generate mel-spectrogram from text, and then synthesize speech from themel-spectrogram using vocoder such as WaveNet. Compared with traditionalconcatenative and statistical parametric approaches, neural network based end-to-end models suffer from slow inference speed, and the synthesized speech isusually not robust (i.e., some words are skipped or repeated) and lack of con-trollability (voice speed or prosody control).

Prominent methods (e.g., Tacotron 2) usually first generate mel-spectrogram from text, and then synthesize speech from the mel-spectrogram using vocoder such as WaveNet. Compared with traditional concatenative and statistical parametric approaches, neural network based end-to-end models suffer from slow inference speed, and the synthesized speech is usually not robust (i.e., some words are skipped or repeated) and lack of con-trollability (voice speed or prosody control).

Text to speech (TTS) has attracted a lot of attention recently due to advancements in deep learning. Neural network-based TTS models (such as Tacotron 2, DeepVoice 3 and Transformer TTS) have outperformed conventional concatenative and statistical parametric approaches in terms of speech quality. Neural network-based TTS models usually first generate a mel-scale spectrogram (or mel-spectrogram) autoregressively from text input and then synthesize speech from the mel-spectrogram using a vocoder. A spectrogram is a visual representation of frequencies measured over time.) To address the above problems, researchers from Microsoft and Zhejiang University propose FastSpeech, a novel feed-forward network that generates mel-spectrograms with fast generation speed, robustness, controllability, and high quality.