Emotional speech synthesis based on improved codebook mapping voice conversion

This paper presents a spectral transformation method for emotional speech synthesis based on voice conversion framework. Three emotions are studied, including anger, happiness and sadness. For the sake of high naturalness, superior speech quality and emotion expressiveness, our original STASC system is modified by introducing a new feature selection strategy and hierarchical codebook mapping procedure. Our result shows that the LSF coefficients at low frequency carry more emotion-relative information, and therefore only these coefficients are converted. Listening tests prove that the proposed method can achieve a satisfactory balance between emotional expression and speech quality of converted speech signals.