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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 Page: 1 2 3 4 5 Hits 1 – 20 of 81 1 MAGIC DUST FOR CROSS-LINGUAL ADAPTATION OF MONOLINGUAL WAV2VEC-2.0 Khurana, Sameer; Laurent, Antoine; Glass, James In: ICASSP 2022 ; https://hal.archives-ouvertes.fr/hal-03544515 ; ICASSP 2022, May 2022, Singapour, Singapore (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 Learning Audio-Video Language Representations Rouditchenko, Andrew. - : Massachusetts Institute of Technology, 2021 BASE Show details 4 Cascaded Multilingual Audio-Visual Learning from Videos ... Rouditchenko, Andrew; Boggust, Angie; Harwath, David. - : arXiv, 2021 BASE Show details 5 Magic dust for cross-lingual adaptation of monolingual wav2vec-2.0 ... Khurana, Sameer; Laurent, Antoine; Glass, James. - : arXiv, 2021 BASE Show details 6 Text-Free Image-to-Speech Synthesis Using Learned Segmental Units ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Glass, James; Harwath, David. - : Underline Science Inc., 2021 BASE Show details 7 Exposure Bias versus Self-Recovery: Are Distortions Really Incremental for Autoregressive Text Generation? ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Glass, James; He, Tianxing. - : Underline Science Inc., 2021 BASE Show details 8 Mitigating Biases in Toxic Language Detection through Invariant Rationalization ... Chuang, Yung-Sung; Gao, Mingye; Luo, Hongyin. - : arXiv, 2021 BASE Show details 9 Mitigating Biases in Toxic Language Detection through Invariant Rationalization ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Chen, Yun-Nung; Chuang, Yung-Sung. - : Underline Science Inc., 2021 BASE Show details 10 A Convolutional Deep Markov Model for Unsupervised Speech Representation Learning Khurana, Sameer; Laurent, Antoine; Hsu, Wei-Ning... In: Interspeech 2020 ; https://hal.archives-ouvertes.fr/hal-02912029 ; Interspeech 2020, Oct 2020, Shanghai, China (2020) BASE Show details 11 Similarity Analysis of Contextual Word Representation Models ... Wu, John M.; Belinkov, Yonatan; Sajjad, Hassan. - : arXiv, 2020 BASE Show details 12 CSTNet: Contrastive Speech Translation Network for Self-Supervised Speech Representation Learning ... Khurana, Sameer; Laurent, Antoine; Glass, James. - : arXiv, 2020 BASE Show details 13 A Convolutional Deep Markov Model for Unsupervised Speech Representation Learning ... Khurana, Sameer; Laurent, Antoine; Hsu, Wei-Ning. - : arXiv, 2020 BASE Show details 14 What Was Written vs. Who Read It: News Media Profiling Using Text Analysis and Social Media Context ... Baly, Ramy; Karadzhov, Georgi; An, Jisun. - : arXiv, 2020 BASE Show details 15 Vector-Quantized Autoregressive Predictive Coding ... Chung, Yu-An; Tang, Hao; Glass, James. - : arXiv, 2020 Abstract: Autoregressive Predictive Coding (APC), as a self-supervised objective, has enjoyed success in learning representations from large amounts of unlabeled data, and the learned representations are rich for many downstream tasks. However, the connection between low self-supervised loss and strong performance in downstream tasks remains unclear. In this work, we propose Vector-Quantized Autoregressive Predictive Coding (VQ-APC), a novel model that produces quantized representations, allowing us to explicitly control the amount of information encoded in the representations. By studying a sequence of increasingly limited models, we reveal the constituents of the learned representations. In particular, we confirm the presence of information with probing tasks, while showing the absence of information with mutual information, uncovering the model's preference in preserving speech information as its capacity becomes constrained. We find that there exists a point where phonetic and speaker information are amplified to ... 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/2005.08392 https://dx.doi.org/10.48550/arxiv.2005.08392 BASE Hide details 16 Non-Autoregressive Predictive Coding for Learning Speech Representations from Local Dependencies ... Liu, Alexander H.; Chung, Yu-An; Glass, James. - : arXiv, 2020 BASE Show details 17 Improved Speech Representations with Multi-Target Autoregressive Predictive Coding ... Chung, Yu-An; Glass, James. - : arXiv, 2020 BASE Show details 18 Classifying Alzheimer's Disease Using Audio and Text-Based Representations of Speech Haulcy, R'mani(R'mani Symon); Glass, James R In: Frontiers (2020) BASE Show details 19 Identification of digital voice biomarkers for cognitive health Lin, Honghuang; Karjadi, Cody; Ang, Ting F. A.... In: Explor Med (2020) BASE Show details 20 On the Linguistic Representational Power of Neural Machine Translation Models Belinkov, Yonatan; Durrani, Nadir; Dalvi, Fahim... In: Computational Linguistics, Vol 46, Iss 1, Pp 1-52 (2020) (2020) BASE Show details Page: 1 2 3 4 5

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