Acoustic modeling and parameter generation using relevance vector machines for speech synthesis

In this paper, we propose a relevance vector machine (RVM) for modeling and generation of a speech feature sequence. In the conventional method, the mean parameter of the hidden Markov model (HMM) state can not consider temporal correlation among corresponding data frames. Since the RVM can be utilized to solve a nonlinear regression problem, we apply it to replace the model parameters of the state output distributions. In the proposed system, RVMs are employed to model the statistically representative process of the state or phone segment which is obtained from normalized training feature sequences by using the semi-parametric nonlinear regression method. We conducted comparative experiments for the proposed RVMs with conventional HMM. It is shown that the proposed state-level RVM-based method performed better than the conventional technique.