To improve the performance of human-computer interaction interfaces, emotion is considered to be one of the most important factors. The major objective of expressive speech synthesis is to inject various expressions r...To improve the performance of human-computer interaction interfaces, emotion is considered to be one of the most important factors. The major objective of expressive speech synthesis is to inject various expressions reflecting different emotions to the synthesized speech. To effectively model and control the emotion, emotion intensity is introduced for expressive speech synthesis model to generate speech conveyed the delicate and complicate emotional states. The system was composed of an emotion analysis module with the goal of extracting control emotion intensity vector and a speech synthesis module responsible for mapping text characters to speech waveform. The proposed continuous variable “perception vector” is a data-driven approach of controlling the model to synthesize speech with different emotion intensities. Compared with the system using a one-hot vector to control emotion intensity, this model using perception vector is able to learn the high-level emotion information from low-level acoustic features. In terms of the model controllability and flexibility, both the objective and subjective evaluations demonstrate perception vector outperforms one-hot vector.展开更多
基金the results of the research project funded by Natural Science Foundation of Hebei University of Economics and Business (No. 2016KYQ05).
文摘To improve the performance of human-computer interaction interfaces, emotion is considered to be one of the most important factors. The major objective of expressive speech synthesis is to inject various expressions reflecting different emotions to the synthesized speech. To effectively model and control the emotion, emotion intensity is introduced for expressive speech synthesis model to generate speech conveyed the delicate and complicate emotional states. The system was composed of an emotion analysis module with the goal of extracting control emotion intensity vector and a speech synthesis module responsible for mapping text characters to speech waveform. The proposed continuous variable “perception vector” is a data-driven approach of controlling the model to synthesize speech with different emotion intensities. Compared with the system using a one-hot vector to control emotion intensity, this model using perception vector is able to learn the high-level emotion information from low-level acoustic features. In terms of the model controllability and flexibility, both the objective and subjective evaluations demonstrate perception vector outperforms one-hot vector.
