  Contemporary Human Computer Interaction (HCI) research relies primarily on
neural network models for machine vision and speech understanding of a system
user. Such models require extensively annotated training datasets for optimal
performance and when building interfaces for users from a vulnerable population
such as young children, GDPR introduces significant complexities in data
collection, management, and processing. Motivated by the training needs of an
Edge AI smart toy platform this research explores the latest advances in
generative neural technologies and provides a working proof of concept of a
controllable data generation pipeline for speech driven facial training data at
scale. In this context, we demonstrate how StyleGAN2 can be finetuned to create
a gender balanced dataset of children's faces. This dataset includes a variety
of controllable factors such as facial expressions, age variations, facial
poses, and even speech-driven animations with realistic lip synchronization. By
combining generative text to speech models for child voice synthesis and a 3D
landmark based talking heads pipeline, we can generate highly realistic,
entirely synthetic, talking child video clips. These video clips can provide
valuable, and controllable, synthetic training data for neural network models,
bridging the gap when real data is scarce or restricted due to privacy
regulations.
