Vowel creation by articulatory control in HMM-based parametric speech synthesis
Zhen-Hua Ling, Korin Richmond, Junichi Yamagishi · 2012
Hidden Markov model (HMM)-based parametric speech synthesis has become a mainstream speech synthesis method in recent years. This method is able to synthesise highly intelligible and smooth speech sounds. In addition, it makes speech synthesis far more flexible compared to the conventional unit selection and waveform concatenation approach. Several adaptation and interpolation methods have been applied to control model parameters and so diversify the characteristics of the generated speech [1]. However, this flexibility relies upon data-driven machine learning algorithms and it is difficult to integrate phonetic knowledge into the system directly when corresponding training data is not available. In previous work, we have proposed a method to improve the flexibility of HMM-based parametric speech synthesis further by integrating articulatory features [2]. Here, we use “articulatory features ” to refer to the continuous