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In recent years multilayer perceptrons (MLPs) with many hid- den layers DeepNeural Network (DNN) has performed sur- prisingly well in many speech tasks,i.e. speech recognition, speaker verification, speech synthesis etc. Althoughin the context of F0 modeling these techniques has not been ex- ploitedproperly. In this paper, Deep Belief Network (DBN), a class of DNN family hasbeen employed and applied to model the F0 contour of synthesized speech whichwas generated by HMM-based speech synthesis system. The experiment was done onBengali language. Several DBN-DNN architectures ranging from four to sevenhidden layers and up to 200 hid- den units per hidden layer was presented andevaluated. The results were compared against clustering tree techniques pop-ularly found in statistical parametric speech synthesis. We show that fromtextual inputs DBN-DNN learns a high level structure which in turn improves F0contour in terms of ob- jective and subjective tests.

F0 Modeling In Hmm-Based Speech Synthesis System Using Deep Belief Network