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Federated learning enables collaborative training of machine learning models under strict privacy restrictions and federated text-to-speech aims to synthesize natural speech of multiple users with a few audio training samples stored in their devices locally.
Federated optimization: Distributed machine learning for on-device intelligence
Jakub Konečnỳ, H Brendan McMahan, Daniel Ramage, and Peter Richtárik · 2016
Earlier work this paper cites.
Learning without forgetting
Zhizhong Li and Derek Hoiem · 2017
Earlier work this paper cites.
Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Cstr vctk corpus: English multi-speaker corpus for cstr voice cloning toolkit
Christophe Veaux, Junichi Yamagishi, Kirsten MacDonald, et al · 2017
Earlier work this paper cites.
Federated learning for mobile keyboard prediction
Andrew Hard, Kanishka Rao, Rajiv Mathews, Swaroop Ramaswamy, Françoise Beaufays, Sean Augenstein, Hubert Eichner, Chloé Kiddon, and Daniel Ramage · 2018
Earlier work this paper cites.
Transfer learning from speaker verification to multispeaker text-to-speech synthesis
Ye Jia, Yu Zhang, Ron Weiss, Quan Wang, Jonathan Shen, Fei Ren, Patrick Nguyen, Ruoming Pang, Ignacio Lopez Moreno, Yonghui Wu, et al · 2018
Earlier work this paper cites.
Packnet: Adding multiple tasks to a single network by iterative pruning
Arun Mallya and Svetlana Lazebnik · 2018
Cited alongside, same era.
Piggyback: Adapting a single network to multiple tasks by learning to mask weights
Arun Mallya, Dillon Davis, and Svetlana Lazebnik · 2018
Cited alongside, same era.
Natural tts synthesis by conditioning wavenet on mel spectrogram predictions
Jonathan Shen, Ruoming Pang, Ron J Weiss, Mike Schuster, Navdeep Jaitly, Zongheng Yang, Zhifeng Chen, Yu Zhang, Yuxuan Wang, Rj Skerrv-Ryan, et al · 2018
Cited alongside, same era.
Generalized end-to-end loss for speaker verification
Li Wan, Quan Wang, Alan Papir, and Ignacio Lopez Moreno · 2018
Cited alongside, same era.
Compacting, picking and growing for unforgetting continual learning
Ching-Yi Hung, Cheng-Hao Tu, Cheng-En Wu, Chien-Hung Chen, Yi-Ming Chan, and Chu-Song Chen · 2019
Cited alongside, same era.
Federated learning for keyword spotting
Fastspeech: Fast, robust and controllable text to speech
Yi Ren, Yangjun Ruan, Xu Tan, Tao Qin, Sheng Zhao, Zhou Zhao, and Tie-Yan Liu · 2019
Later among the works it cites.
Experience replay for continual learning
David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy Lillicrap, and Gregory Wayne · 2019
Later among the works it cites.
Glow-tts: A generative flow for text-to-speech via monotonic alignment search
Jaehyeon Kim, Sungwon Kim, Jungil Kong, and Sungroh Yoon · 2020
Later among the works it cites.
Fastspeech 2: Fast and high-quality end-to-end text-to-speech
Yi Ren, Chenxu Hu, Tao Qin, Sheng Zhao, Zhou Zhao, and Tie-Yan Liu · 2020
Later among the works it cites.
Fetchsgd: Communication-efficient federated learning with sketching
Daniel Rothchild, Ashwinee Panda, Enayat Ullah, Nikita Ivkin, Ion Stoica, Vladimir Braverman, Joseph Gonzalez, and Raman Arora · 2020
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David Leroy, Alice Coucke, Thibaut Lavril, Thibault Gisselbrecht, and Joseph Dureau · 2019
Cited alongside, same era.
A survey on federated learning systems: vision, hype and reality for data privacy and protection
Qinbin Li, Zeyi Wen, Zhaomin Wu, Sixu Hu, Naibo Wang, and Bingsheng He · 2019
Cited alongside, same era.
Parallel wavegan: A fast waveform generation model based on generative adversarial networks with multi-resolution spectrogram
Ryuichi Yamamoto, Eunwoo Song, and Jae-Min Kim · 2020
Later among the works it cites.