Factorized context modelling for Text-to-Speech synthesis

Because speech units are so context-dependent, a large number of linguistic context features are generally used by HMM-based Text-to-Speech (TTS) speech synthesis systems, via context-dependent models. Since it is impossible to train separate models for every context, decision trees are used to discover the most important combinations of features that should be modelled. The task of the decision tree is very hard - to generalize from a very small observed part of the context feature space to the rest - and they have a major weakness: they cannot directly take advantage of factorial properties: they subdivide the model space based on one feature at a time. We propose a Dynamic Bayesian Network (DBN) based Mixed Memory Markov Model (MMMM) to provide factorization of the context space. The results of a listening test are provided as evidence that the model successfully learns the factorial nature of this space.