F0 Contour Prediction With A Deep Belief Network-Gaussian Process Hybrid Model

In this work we look at using non-parametric, exemplar-based regression for the prediction of prosodic contour targets from textual features in a speech synthesis system. We investigate the performance of Gaussian Process regression on this task when the covariance kernel operates on a variety of input feature spaces. In particular, we consider non-linear features extracted via Deep Belief Networks. We motivate the use of this hybrid model by considering the initial deep-layer model as a feature extractor that can summarize high-level structure from the raw inputs to improve the regression of an exemplar-based model in the second part of the approach. By looking at both objective metrics and perceptual listening tests, we evaluate these proposals against each other, and against the standard clustering-tree techniques implemented in parametric synthesis for the prediction of prosodic targets.