Exploring How Generative Adversarial Networks Learn Phonological Representations

21 May 2023

Papers citing "Exploring How Generative Adversarial Networks Learn Phonological Representations"

9 / 9 papers shown

Title
Generative Adversarial Networks Gilad Cohen Raja Giryes GAN 134 30,021 0 01 Mar 2022
HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units Wei-Ning Hsu Benjamin Bolte Yao-Hung Hubert Tsai Kushal Lakhotia Ruslan Salakhutdinov Abdel-rahman Mohamed SSL 123 2,879 0 14 Jun 2021
wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations Alexei Baevski Henry Zhou Abdel-rahman Mohamed Michael Auli SSL 160 5,677 0 20 Jun 2020
CiwGAN and fiwGAN: Encoding information in acoustic data to model lexical learning with Generative Adversarial Networks Gašper Beguš GAN 39 34 0 04 Jun 2020
Neural Language Models as Psycholinguistic Subjects: Representations of Syntactic State Richard Futrell Ethan Gotlieb Wilcox Takashi Morita Peng Qian Miguel Ballesteros R. Levy MILM 116 193 0 08 Mar 2019
Targeted Syntactic Evaluation of Language Models Rebecca Marvin Tal Linzen 58 414 0 27 Aug 2018
Colorless green recurrent networks dream hierarchically Kristina Gulordava Piotr Bojanowski Edouard Grave Tal Linzen Marco Baroni 66 503 0 29 Mar 2018
WaveNet: A Generative Model for Raw Audio Aaron van den Oord Sander Dieleman Heiga Zen Karen Simonyan Oriol Vinyals Alex Graves Nal Kalchbrenner A. Senior Koray Kavukcuoglu DiffM 275 7,361 0 12 Sep 2016
Audio Word2Vec: Unsupervised Learning of Audio Segment Representations using Sequence-to-sequence Autoencoder Yu-An Chung Chao-Chung Wu Chia-Hao Shen Hung-yi Lee Lin-Shan Lee AI4TS 55 182 0 03 Mar 2016