  The quest for comprehensive generative models of intonation that link
linguistic and paralinguistic functions to prosodic forms has been a
longstanding challenge of speech communication research. Traditional intonation
models have given way to the overwhelming performance of deep learning (DL)
techniques for training general purpose end-to-end mappings using millions of
tunable parameters. The shift towards black box machine learning models has
nonetheless posed the reverse problem -- a compelling need to discover
knowledge, to explain, visualise and interpret. Our work bridges between a
comprehensive generative model of intonation and state-of-the-art DL
techniques. We build upon the modelling paradigm of the Superposition of
Functional Contours (SFC) model and propose a Variational Prosody Model (VPM)
that uses a network of variational contour generators to capture the
context-sensitive variation of the constituent elementary prosodic contours. We
show that the VPM can give insight into the intrinsic variability of these
prosodic prototypes through learning a meaningful prosodic latent space
representation structure. We also show that the VPM is able to capture prosodic
phenomena that have multiple dimensions of context based variability. Since it
is based on the principle of superposition, the VPM does not necessitate the
use of specially crafted corpora for the analysis, opening up the possibilities
of using big data for prosody analysis. In a speech synthesis scenario, the
model can be used to generate a dynamic and natural prosody contour that is
devoid of averaging effects.
