Voice command generation using Progressive Wavegans
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.
Cite
@article{arxiv.1903.07395,
title = {Voice command generation using Progressive Wavegans},
author = {Thomas Wiest and Nicholas Cummins and Alice Baird and Simone Hantke and Judith Dineley and Björn Schuller},
journal= {arXiv preprint arXiv:1903.07395},
year = {2019}
}
Comments
7 pages, 2 figures