Controlling High-Dimensional Data With Sparse Input

We address the problem of human-in-the-loop control for generating highly-structured data. This task is challenging because existing generative models lack an efficient interface through which users can modify the output. Users have the option to either manually explore a non-interpretable latent space, or to laboriously annotate the data with conditioning labels. To solve this, we introduce a novel framework whereby an encoder maps a sparse, human interpretable control space onto the latent space of a generative model. We apply this framework to the task of controlling prosody in text-to-speech synthesis. We propose a model, called Multiple-Instance CVAE (MICVAE), that is specifically designed to encode sparse prosodic features and output complete waveforms. We show empirically that MICVAE displays desirable qualities of a sparse human-in-the-loop control mechanism: efficiency, robustness, and faithfulness. With even a very small number of input values (~4), MICVAE enables users to improve the quality of the output significantly, in terms of listener preference (4:1).