Syllable-Level Representations of Suprasegmental Features for DNN-Based Text-to-Speech Synthesis

A top-down hierarchical system based on deep neural networks is investigated for the modeling of prosody in speech synthesis. Suprasegmental features are processed separately from segmental features and a compact distributed representation of high-level units is learned at syllable-level. The suprasegmental representation is then integrated into a frame-level network. Objective measures show that balancing segmental and suprasegmental features can be useful for the frame-level network. Additional features incorporated into the hierarchical system are then tested. At the syllable-level, a bag-of-phones representation is proposed and, at the word-level, embeddings learned from text sources are used. It is shown that the hierarchical system is able to leverage new features at higher-levels more efficiently than a system which exploits them directly at the frame-level. A perceptual evaluation of the proposed systems is conducted and followed by a discussion of the results.