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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 11 of 11 1 Measuring the Impact of Individual Domain Factors in Self-Supervised Pre-Training ... Sanabria, Ramon; Hsu, Wei-Ning; Baevski, Alexei. - : arXiv, 2022 BASE Show details 2 Simple and Effective Unsupervised Speech Synthesis ... Liu, Alexander H.; Lai, Cheng-I Jeff; Hsu, Wei-Ning. - : arXiv, 2022 BASE Show details 3 Textless Speech-to-Speech Translation on Real Data ... Lee, Ann; Gong, Hongyu; Duquenne, Paul-Ambroise. - : arXiv, 2021 BASE Show details 4 HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units ... Hsu, Wei-Ning; Bolte, Benjamin; Tsai, Yao-Hung Hubert. - : arXiv, 2021 BASE Show details 5 Textless Speech Emotion Conversion using Discrete and Decomposed Representations ... Kreuk, Felix; Polyak, Adam; Copet, Jade. - : arXiv, 2021 BASE Show details 6 A Convolutional Deep Markov Model for Unsupervised Speech Representation Learning ... Khurana, Sameer; Laurent, Antoine; Hsu, Wei-Ning. - : arXiv, 2020 BASE Show details 7 Learning Hierarchical Discrete Linguistic Units from Visually-Grounded Speech ... Harwath, David; Hsu, Wei-Ning; Glass, James. - : arXiv, 2019 BASE Show details 8 Transfer Learning from Audio-Visual Grounding to Speech Recognition ... Hsu, Wei-Ning; Harwath, David; Glass, James. - : arXiv, 2019 Abstract: Transfer learning aims to reduce the amount of data required to excel at a new task by re-using the knowledge acquired from learning other related tasks. This paper proposes a novel transfer learning scenario, which distills robust phonetic features from grounding models that are trained to tell whether a pair of image and speech are semantically correlated, without using any textual transcripts. As semantics of speech are largely determined by its lexical content, grounding models learn to preserve phonetic information while disregarding uncorrelated factors, such as speaker and channel. To study the properties of features distilled from different layers, we use them as input separately to train multiple speech recognition models. Empirical results demonstrate that layers closer to input retain more phonetic information, while following layers exhibit greater invariance to domain shift. Moreover, while most previous studies include training data for speech recognition for feature extractor training, our ... : Accepted to Interspeech 2019. 4 pages, 2 figures ... Keyword: Audio and Speech Processing eess.AS; Computation and Language cs.CL; FOS Computer and information sciences; FOS Electrical engineering, electronic engineering, information engineering; Machine Learning cs.LG; Sound cs.SD URL: https://arxiv.org/abs/1907.04355 https://dx.doi.org/10.48550/arxiv.1907.04355 BASE Hide details 9 Unsupervised Adaptation with Interpretable Disentangled Representations for Distant Conversational Speech Recognition ... Hsu, Wei-Ning; Tang, Hao; Glass, James. - : arXiv, 2018 BASE Show details 10 Unsupervised Representation Learning of Speech for Dialect Identification ... Shon, Suwon; Hsu, Wei-Ning; Glass, James. - : arXiv, 2018 BASE Show details 11 Unsupervised Learning of Disentangled and Interpretable Representations from Sequential Data ... Hsu, Wei-Ning; Zhang, Yu; Glass, James. - : arXiv, 2017 BASE Show details

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