Modeling F0 trajectories in hierarchically structured deep neural networks
We present a F0 modeling which considers the intrinsic F0 property using deep neural networks (DNN) for statistical parametric speech synthesis. The F0 trajectories are parameterized using optimized discrete cosine transform (DCT) analysis to embody the long-term F0 property. A group of DNNs are utilized to describe contributions of context features at different prosodic levels to the observed F0 contours considering the additive nature of F0 generation. Two structures, cascade and parallel DNNs, are proposed and compared in our experiments.
deep neural network speech synthesis discrete cosine transform hidden markov model fundamental frequency