FCH-TTS: Fast, Controllable and High-quality Non-Autoregressive Text-to-Speech Synthesis

Abstract

Inspired by the success of the non-autoregressive speech synthesis model FastSpeech, we propose FCH-TTS, a fast, controllable and universal neural text-to-speech (TTS) capable of generating high-quality spectrograms. The basic architecture of FCH-TTS is similar to that of FastSpeech, but FCH-TTS uses a simple yet effective attention-based soft alignment mechanism to replace the complex teacher model in FastSpeech, allowing the model to be better adapted to different languages. Specifically, in addition to the control of voice speed and prosody, a fusion module has been designed to better model speaker features in order to obtain the desired timbre. Meanwhile, several special loss functions were applied to ensure the quality of the output mel-spectrogram. Experimental results on the dataset LJSpeech show that FCH-TTS achieves the fastest inference speed compared to all baseline models, while also achieving the best speech quality. In addition, the controllability of the model with respect to prosody, voice speed and timbre was validated on several datasets, and the good performance on the low-resource Tibetan dataset demonstrates the universality of the model.