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The prosodic aspects of speech signals produced by current text-to-speechsystems are typically averaged over training material, and as such lack thevariety and liveliness found in natural speech. To avoid monotony and averagedprosody contours, it is desirable to have a way of modeling the variation inthe prosodic aspects of speech, so audio signals can be synthesized in multipleways for a given text. We present a new, hierarchically structured conditionalvariational autoencoder to generate prosodic features (fundamental frequency,energy and duration) suitable for use with a vocoder or a generative model likeWaveNet. At inference time, an embedding representing the prosody of a sentencemay be sampled from the variational layer to allow for prosodic variation. Toefficiently capture the hierarchical nature of the linguistic input (words,syllables and phones), both the encoder and decoder parts of the auto-encoderare hierarchical, in line with the linguistic structure, with layers beingclocked dynamically at the respective rates. We show in our experiments thatour dynamic hierarchical network outperforms a non-hierarchicalstate-of-the-art baseline, and, additionally, that prosody transfer acrosssentences is possible by employing the prosody embedding of one sentence togenerate the speech signal of another.

CHiVE: Varying Prosody in Speech Synthesis with a Linguistically Driven Dynamic Hierarchical Conditional Variational Network