An Emotion Speech Synthesis Method Based on VITS

Abstract

People and things can be connected through the Internet of Things (IoT), and speech synthesis is one of the key technologies. At this stage, end-to-end speech synthesis systems are capable of synthesizing relatively realistic human voices, but the current commonly used parallel text-to-speech suffers from loss of useful information during the two-stage delivery process, and the control features of the synthesized speech are monotonous, with insufficient expression of features, including emotion, leading to emotional speech synthesis becoming a challenging task. In this paper, we propose a new system named Emo-VITS, which is based on the highly expressive speech synthesis module VITS, to realize the emotion control of text-to-speech synthesis. We designed the emotion network to extract the global and local features of the reference audio, and then fused the global and local features through the emotion feature fusion module based on the attention mechanism, so as to achieve more accurate and comprehensive emotion speech synthesis. The experimental results show that the Emo-VITS system’s error rate went up a little bit compared with the network without emotionality and does not affect the semantic understanding. However, this system is superior to other networks in naturalness, sound quality, and emotional similarity.