Simultaneous Optimization of Multiple Tree-Based Factor Analyzed HMM for Speech Synthesis

This paper proposes a novel method to build multiple decision trees as a structure of factor analyzed hidden Markov model for speech synthesis. In the proposed method, the multiple decision trees grow simultaneously rather than sequentially to take into account the relationship between the trees. However, the simultaneous growing is computationally infeasible due to an exponential increase in the number of tree structures to be evaluated. To solve the problem, we further propose two computational complexity reduction algorithms that achieve a significant reduction in the computational time. Experimental results show that the proposed method outperforms the conventional one based on a single decision tree.