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