  This paper models the fundamental frequency contours on both Mandarin and
Cantonese speech with decision trees and DNNs (deep neural networks). Different
kinds of f0 representations and model architectures are tested for decision
trees and DNNs. A new model called Additive-BLSTM (additive bidirectional long
short term memory) that predicts a base f0 contour and a residual f0 contour
with two BLSTMs is proposed. With respect to objective measures of RMSE and
correlation, applying tone-dependent trees together with sample normalization
and delta feature regularization within decision tree framework performs best.
While the new Additive-BLSTM model with delta feature regularization performs
even better. Subjective listening tests on both Mandarin and Cantonese
comparing Random Forest model (multiple decision trees) and the Additive-BLSTM
model were also held and confirmed the advantage of the new model according to
the listeners' preference.
