English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We propose AudioStyleGAN (ASGAN), a new generative adversarial network (GAN)for unconditional speech synthesis. As in the StyleGAN family of imagesynthesis models, ASGAN maps sampled noise to a disentangled latent vectorwhich is then mapped to a sequence of audio features so that signal aliasing issuppressed at every layer. To successfully train ASGAN, we introduce a numberof new techniques, including a modification to adaptive discriminatoraugmentation to probabilistically skip discriminator updates. ASGAN achievesstate-of-the-art results in unconditional speech synthesis on the Google SpeechCommands dataset. It is also substantially faster than the top-performingdiffusion models. Through a design that encourages disentanglement, ASGAN isable to perform voice conversion and speech editing without being explicitlytrained to do so. ASGAN demonstrates that GANs are still highly competitivewith diffusion models. Code, models, samples:https://github.com/RF5/simple-asgan/.

GAN You Hear Me? Reclaiming Unconditional Speech Synthesis from Diffusion Models