Book ; Online: Controlling High-Dimensional Data With Sparse Input
2023
Abstract: 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 ... ...
Abstract | 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). Comment: 11 pages |
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Keywords | Electrical Engineering and Systems Science - Audio and Speech Processing ; Computer Science - Artificial Intelligence ; Computer Science - Computation and Language ; Computer Science - Machine Learning |
Subject code | 004 |
Publishing date | 2023-03-14 |
Publishing country | us |
Document type | Book ; Online |
Database | BASE - Bielefeld Academic Search Engine (life sciences selection) |
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