Voice command generation using progressive WaveGANs
- 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.
| Author: | Nicholas CumminsORCiDGND, T. Wiest, Alice BairdGND, Simone Hantke, J. Dineley, Björn SchullerORCiDGND |
|---|---|
| URN: | urn:nbn:de:bvb:384-opus4-668747 |
| Frontdoor URL | https://opus.bibliothek.uni-augsburg.de/opus4/66874 |
| Parent Title (English): | arXiv |
| Type: | Preprint |
| Language: | English |
| Date of Publication (online): | 2019/12/09 |
| Year of first Publication: | 2019 |
| Publishing Institution: | Universität Augsburg |
| Release Date: | 2019/12/11 |
| First Page: | arXiv: 1903.07395 |
| DOI: | https://doi.org/10.48550/arXiv.1903.07395 |
| Institutes: | Fakultät für Angewandte Informatik |
| Fakultät für Angewandte Informatik / Institut für Informatik | |
| Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Embedded Intelligence for Health Care and Wellbeing | |
| Dewey Decimal Classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik |
| Licence (German): |  Deutsches Urheberrecht |
