Parallel and cascaded deep neural networks for text-to-speech synthesis

9th ISCA Workshop on Speech Synthesis Workshop (SSW 9)

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.