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End-to-End Adversarial Text-to-Speech

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DeepMind: In our new paper [https://arxiv.org/abs/2006.03575] we propose EATS: End-to-End Adversarial Text-to-Speech, which allows for speech synthesis directly from text or phonemes without the need for multi-stage training pipelines or additional supervision. Audio: https://bit.ly/2Ya9rRK https://t.co/h8Ye4FfC0M

8 replies, 740 likes


Sander Dieleman: Our latest work on GANs for text-to-speech, from characters/phonemes to waveforms with a single model. Learning varying alignment without teacher forcing is tricky, but we found dynamic time warping (DTW) to be very effective.

2 replies, 163 likes


Sander Dieleman: We've updated the EATS paper on arXiv: https://arxiv.org/abs/2006.03575 'End-to-end' has many possible interpretations – Table 5 in the appendix (p. 21) describes some of the many ways in which the TTS pipeline has been factorised into stages in the literature, for easier comparison. https://t.co/ku0K7EJ5QA

2 replies, 82 likes


AiNews.page: Tweet of the day #TextToSpeech #DeepMind #NLP

0 replies, 2 likes


Vishal Chandra 👻: I hereby reserve the title of my deep learning research paper as follows: Fast Adversarial Reinforcement-learning for Text-to-Speech

0 replies, 1 likes


Content

Found on Jun 08 2020 at https://arxiv.org/pdf/2006.03575.pdf

PDF content of a computer science paper: End-to-End Adversarial Text-to-Speech