Fetching the paper…
Reading the bibliography…
Humans encode information into sounds by controlling articulators and decode information from sounds using the auditory apparatus.
Modern Applied Statistics with S
W. N. Venables and B. D. Ripley, · 2002
Earlier work this paper cites.
“Computing and visualizing dynamic time warping alignments in R: The dtw package,”
Toni Giorgino, · 2009
Earlier work this paper cites.
“Announcing the electromagnetic articulography (day 1) subset of the mngu0 articulatory corpus,”
Korin Richmond, Phil Hoole, and Simon King, · 2011
Earlier work this paper cites.
“Robust articulatory speech synthesis using deep neural networks for BCI applications,”
Florent Bocquelet, Thomas Hueber, Laurent Girin, Pierre Badin, and Blaise Yvert, · 2014
Earlier work this paper cites.
“Generative adversarial nets,”
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, · 2014
Earlier work this paper cites.
“InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets,”
Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel, · 2016
Earlier work this paper cites.
“Data driven articulatory synthesis with deep neural networks,”
Sandesh Aryal and Ricardo Gutierrez-Osuna, · 2016
Earlier work this paper cites.
“Unsupervised representation learning with deep convolutional generative adversarial networks,”
Alec Radford, Luke Metz, and Soumith Chintala, · 2016
Earlier work this paper cites.
“Wasserstein generative adversarial networks,”
Martin Arjovsky, Soumith Chintala, and Léon Bottou, · 2017
Earlier work this paper cites.
“Adversarial audio synthesis,”
Chris Donahue, Julian J. McAuley, and Miller S. Puckette, · 2019
Cited alongside, same era.
“Generative adversarial phonology: Modeling unsupervised phonetic and phonological learning with neural networks,”
Gašper Beguš, · 2020
Cited alongside, same era.
“Towards an articulatory-driven neural vocoder for speech synthesis,”
Marc-Antoine Georges, Pierre Badin, Julien Diard, Laurent Girin, Jean-Luc Schwartz, and Thomas Hueber, · 2020
Cited alongside, same era.
“Learning Speech Production and Perception through Sensorimotor Interactions,”
S. Shamma, P. Patel, S. Mukherjee, G. Marion, B. Khalighinejad, C. Han, J. Herrero, S. Bickel, A. Mehta, and N. Mesgarani, · 2020
Cited alongside, same era.
“CiwGAN and fiwGAN: Encoding information in acoustic data to model lexical learning with Generative Adversarial Networks,”
Gašper Beguš, · 2021
Cited alongside, same era.
“Deep Speech Synthesis from Articulatory Representations,”
Peter Wu, Shinji Watanabe, Louis Goldstein, Alan W Black, and Gopala Krishna Anumanchipalli, · 2022
Later among the works it cites.
“Self-supervised speech unit discovery from articulatory and acoustic features using VQ-VAE,”
Marc-Antoine Georges, Jean-Luc Schwartz, and Thomas Hueber, · 2022
Later among the works it cites.
“The Mirrornet: Learning audio synthesizer controls inspired by sensorimotor interaction,”
Yashish M. Siriwardena, Guilhem Marion, and Shihab Shamma, · 2022
Later among the works it cites.
“Articulation GAN: Unsupervised modeling of articulatory learning,”
Gašper Beguš, Alan Zhou, Peter Wu, and Gopala K. Anumanchipalli, · 2023
Closest in time.
“Learning to Compute the Articulatory Representations of Speech with the MIRRORNET,”
Yashish M Siriwardena, Carol Espy-Wilson, and Shihab Shamma, · 2023
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
“Local and non-local dependency learning and emergence of rule-like representations in speech data by deep convolutional generative adversarial networks,”
Gašper Beguš, · 2021
Cited alongside, same era.
“Identity-based patterns in deep convolutional networks: Generative adversarial phonology and reduplication,”
Gašper Beguš, · 2021
Cited alongside, same era.
“Ema2s: An end-to-end multimodal articulatory-to-speech system,”
Yu-Wen Chen, Kuo-Hsuan Hung, Shang-Yi Chuang, Jonathan Sherman, Wen-Chin Huang, Xugang Lu, and Yu Tsao, · 2021
Cited alongside, same era.
“Modeling speech recognition and synthesis simultaneously: Encoding and decoding lexical and sublexical semantic information into speech with no direct access to speech data,”
Gašper Beguš and Alan Zhou, · 2022
Cited alongside, same era.
Peter Wu, Tingle Li, Yijing Lu, Yubin Zhang, Jiachen Lian, Alan W Black, Louis Goldstein, Shinji Watanabe, and Gopala K. Anumanchipalli, · 2023
Closest in time.
“Robust speech recognition via large-scale weak supervision,”
Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine Mcleavey, and Ilya Sutskever, · 2023
Closest in time.
“Approaching an unknown communication system by latent space exploration and causal inference,”
Gašper Beguš, Andrej Leban, and Shane Gero, · 2023
Closest in time.