An optimization algorithm of independent mean and variance parameter tying structures for HMM-based speech synthesis

This paper proposes a technique for constructing independent parameter tying structures of mean and variance in HMM based speech synthesis. Conventionally, mean and variance parameters are assumed to have the same tying structure. However, it has been reported that a clustering technique of mean vectors while tying all variance matrices improves the quality of synthesized speech. This indicates that mean and variance parameters should have different optimal tying structures. In the proposed technique, the decision trees for mean and variance parameters are simultaneously grown by taking into account the dependency on mean and variance parameters. Experimental results show that the proposed technique outperforms the conventional one.