Parallel and cascaded deep neural networks for text-to-speech synthesis
Manuel Sam Ribeiro, Oliver Watts, Junichi Yamagishi · 2016
An investigation of cascaded and parallel deep neural networks for speech synthesis is conducted.In these systems, suprasegmental linguistic features (syllable-level and above) are processed separately from segmental features (phone-level and below).The suprasegmental component of the networks learns compact distributed representations of high-level linguistic units without any segmental influence.These representations are then integrated into a frame-level system using a cascaded or a parallel approach.In the cascaded network, suprasegmental representations are used as input to the framelevel network.In the parallel network, segmental and suprasegmental features are processed separately and concatenated at a later stage.These experiments are conducted with a standard set of high-dimensional linguistic features as well as a hand-pruned one.It is observed that hierarchical systems are consistently preferred over the baseline feedforward systems.Similarly, parallel networks are preferred over cascaded networks.