{"url":"/method/gan-tts","slug":"gan-tts","name":"GAN-TTS","full_name":"GAN-TTS","full_name_withheld":false,"description_markdown":"**GAN-TTS** is a generative adversarial network for text-to-speech synthesis. The architecture is composed of a conditional feed-forward generator producing raw speech audio, and an ensemble of discriminators which operate on random windows of different sizes. The discriminators analyze the audio both in terms of general realism, as well as how well the audio corresponds to the utterance that should be pronounced.\r\n\r\nThe generator architecture consists of several GBlocks, which are residual based (dilated) [convolution](https://paperswithcode.com/method/convolution) blocks. GBlocks 3–7 gradually upsample the temporal dimension of hidden representations by factors of 2, 2, 2, 3, 5, while the number of channels is reduced by GBlocks 3, 6 and 7 (by a factor of 2 each). The final convolutional layer with [Tanh activation](https://paperswithcode.com/method/tanh-activation) produces a single-channel audio waveform.\r\n\r\nInstead of a single discriminator, GAN-TTS uses an ensemble of Random Window Discriminators (RWDs) which operate on randomly sub-sampled fragments of the real or generated samples. The ensemble allows for the evaluation of audio in different complementary ways.","description_state":"present","introduced_year":null,"introduced_by":{"title":"High Fidelity Speech Synthesis with Adversarial Networks","paper":"/paper/high-fidelity-speech-synthesis-with-1","first_author":"Mikołaj Bińkowski","n_authors":8,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/high-fidelity-speech-synthesis-with-1"},"source":{"url":"https://arxiv.org/abs/1909.11646v2","title":"High Fidelity Speech Synthesis with Adversarial Networks","url_on_a_paper_host":true},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"Sequential","area_id":"sequential","collection":"Sequence To Sequence Models","url":"/methods/category/sequence-to-sequence-models","pwc_aliases":[]},{"area":"Audio","area_id":"audio","collection":"Text-to-Speech Models","url":"/methods/category/text-to-speech-models","pwc_aliases":[]}],"n_papers_tagged":2,"archive_num_papers":2,"papers_newest_first":[{"paper":"/paper/a-spectral-energy-distance-for-parallel","title":"A Spectral Energy Distance for Parallel Speech Synthesis","date":"2020-08-03","arxiv_id":"2008.01160","n_code_links":2,"syntology":{"ran":0,"of":3,"unverified":3,"pointer_only":0}},{"paper":"/paper/high-fidelity-speech-synthesis-with-1","title":"High Fidelity Speech Synthesis with Adversarial Networks","date":"2019-09-25","arxiv_id":"1909.11646","n_code_links":3,"syntology":{"ran":2,"of":5,"unverified":3,"pointer_only":4}}],"papers_shown":2,"tasks":[{"task":"/task/speech-synthesis","name":"Speech Synthesis","papers":2},{"task":null,"name":"Generative Adversarial Network","papers":1},{"task":"/task/text-to-speech","name":"Text to Speech","papers":1},{"task":"/task/high","name":"Vocal Bursts Intensity Prediction","papers":1},{"task":"/task/scoring-rule","name":"scoring rule","papers":1},{"task":"/task/text-to-speech-1","name":"text-to-speech","papers":1}],"tasks_shown":6,"n_tasks":6,"usage_by_year":[{"year":"2019","papers":1},{"year":"2020","papers":1}],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/gan-tts"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}