  In the last two years, there have been numerous papers that have looked into
using Deep Neural Networks to replace the acoustic model in traditional
statistical parametric speech synthesis. However, far less attention has been
paid to approaches like DNN-based postfiltering where DNNs work in conjunction
with traditional acoustic models. In this paper, we investigate the use of
Recurrent Neural Networks as a potential postfilter for synthesis. We explore
the possibility of replacing existing postfilters, as well as highlight the
ease with which arbitrary new features can be added as input to the postfilter.
We also tried a novel approach of jointly training the Classification And
Regression Tree and the postfilter, rather than the traditional approach of
training them independently.
