Deep Neural Networks (DNNs) have been shown to provide state-of-the-art performance over other baseline models in the task of predicting prosodic targets from text in a speech-synthesis system. However, prosody prediction can be affected by an interaction of short- and long-term contextual factors that a static model that depends on a fixed-size context window can fail to properly capture. In this work, we look at a recurrent formulation of neural networks (RNNs) that are deep in time and can store state information from an arbitrarily large input history when making a prediction. We show that RNNs provide improved performance over DNNs of comparable size in terms of various objective metrics for a variety of prosodic streams (notably, a relative reduction of about 6% in F0 mean-square error accompanied by a relative increase of about 14% in F0 variance), as well as in terms of perceptual quality assessed through mean-opinion-score listening tests.
Bibliographic reference. Fernandez, Raul / Rendel, Asaf / Ramabhadran, Bhuvana / Hoory, Ron (2014): "Prosody contour prediction with long short-term memory, bi-directional, deep recurrent neural networks", In INTERSPEECH-2014, 2268-2272.