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arXiv · 1903.07395

Voice command generation using Progressive Wavegans

Thomas Wiest·Nicholas Cummins·Alice Baird·Simone Hantke·Judith Dineley·Björn Schuller
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Abstract

Generative Adversarial Networks (GANs) have become exceedingly popular in a wide range of data-driven research fields, due in part to their success in image generation. Their ability to generate new samples, often from only a small amount of input data, makes them an exciting research tool in areas with limited data resources. One less-explored application of GANs is the synthesis of speech and audio samples. Herein, we propose a set of extensions to the WaveGAN paradigm, a recently proposed approach for sound generation using GANs. The aim of these extensions - preprocessing, Audio-to-Audio generation, skip connections and progressive structures - is to improve the human likeness of synthetic speech samples. Scores from listening tests with 30 volunteers demonstrated a moderate improvement (Cohen's d coefficient of 0.65) in human likeness using the proposed extensions compared to the original WaveGAN approach.

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Thomas Wiest, Nicholas Cummins, Alice Baird, Simone Hantke, Judith Dineley, Björn Schuller. 2019-03-13. Voice command generation using Progressive Wavegans. https://arxiv.org/abs/1903.07395

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