Mark Hasegawa-Johnson

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Thomas S. HuangUniversity of Illinois, Urbana-ChampaignVerified email at ifp.uiuc.edu
Jennifer ColeProfessor of Linguistics, Northwestern UniversityVerified email at northwestern.edu
Yang ZhangMIT-IBM Watson AI LabVerified email at ibm.com
Xiaodan ZhuangApple Siri; BBN, University of Illinois, IBM, MicrosoftVerified email at apple.com
Po-Sen HuangResearch Scientist, DeepMindVerified email at google.com
Shiyu ChangUniversity of California, Santa BarbaraVerified email at cs.ucsb.edu
Ken ChenProfessor of Bioinformatics and Computational Biology, The University of Texas MD AndersonVerified email at mdanderson.org
Kaizhi QianUniversity of Illinois at Urbana-ChampaignVerified email at illinois.edu
Hao TangApple, Inc.Verified email at apple.com
Raymond A. YehToyota Technological Institute at ChicagoVerified email at ttic.edu
Odette ScharenborgAssociate professor & Delft Technology Fellow, Delft University of Technology, The NetherlandsVerified email at tudelft.nl
Tae-Jin YoonSungshin UniversityVerified email at sungshin.ac.kr
Preethi JyothiAssociate Professor of Computer Science and Engineering, Indian Institute of Technology BombayVerified email at cse.iitb.ac.in
Camille GoudeseuneUniversity of Illinois at Urbana-ChampaignVerified email at illinois.edu
Minh N. DoProfessor, University of Illinois at Urbana-Champaign and VinUniversityVerified email at illinois.edu
Xuesong YangAmazon Alexa SpeechVerified email at amazon.com
Tomohiko TaniguchiFujitsuVerified email at jp.fujitsu.com
Teck-Yian LimUniversity of Illinois at Urbana-ChampaignVerified email at illinois.edu
Paris SmaragdisUniversity of Illinois at Urbana-Champaign / Adobe ResearchVerified email at illinois.edu
Chen ChenAppleVerified email at apple.com

Mark Hasegawa-Johnson

Professor of Electrical and Computer Engineering, University of Illinois

Verified email at illinois.edu - Homepage

Speech Audio Natural Language Processing


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Semantic image inpainting with deep generative models RA Yeh, C Chen, T Yian Lim, AG Schwing, M Hasegawa-Johnson, MN Do Proceedings of the IEEE conference on computer vision and pattern …, 2017	1014	2017
Joint optimization of masks and deep recurrent neural networks for monaural source separation PS Huang, M Kim, M Hasegawa-Johnson, P Smaragdis IEEE/ACM Transactions on Audio, Speech, and Language Processing 23 (12 …, 2015	442	2015
Deep learning for monaural speech separation PS Huang, M Kim, M Hasegawa-Johnson, P Smaragdis 2014 IEEE International Conference on Acoustics, Speech and Signal …, 2014	437	2014
Brain anatomy differences in childhood stuttering SE Chang, KI Erickson, NG Ambrose, MA Hasegawa-Johnson, ... Neuroimage 39 (3), 1333-1344, 2008	399	2008
Singing-voice separation from monaural recordings using robust principal component analysis PS Huang, SD Chen, P Smaragdis, M Hasegawa-Johnson 2012 IEEE International Conference on Acoustics, Speech and Signal …, 2012	386	2012
Semantic image inpainting with perceptual and contextual losses R Yeh, C Chen, TY Lim, M Hasegawa-Johnson, MN Do arXiv preprint arXiv:1607.07539 2 (3), 2016	355	2016
COVID-19 lockdown allows researchers to quantify the effects of human activity on wildlife C Rutz, MC Loretto, AE Bates, SC Davidson, CM Duarte, W Jetz, ... Nature Ecology & Evolution 4 (9), 1156-1159, 2020	300	2020
Dysarthric speech database for universal access research H Kim, M Hasegawa-Johnson, A Perlman, J Gunderson, TS Huang, ... Ninth Annual Conference of the International Speech Communication Association, 2008	242	2008
Dilated recurrent neural networks S Chang, Y Zhang, W Han, M Yu, X Guo, W Tan, X Cui, M Witbrock, ... Advances in neural information processing systems 30, 2017	218	2017
AVICAR: Audio-visual speech corpus in a car environment B Lee, M Hasegawa-Johnson, C Goudeseune, S Kamdar, S Borys, M Liu, ... Eighth International Conference on Spoken Language Processing, 2004	217	2004
Real-world acoustic event detection X Zhuang, X Zhou, MA Hasegawa-Johnson, TS Huang Pattern recognition letters 31 (12), 1543-1551, 2010	207	2010
Regression from patch-kernel S Yan, X Zhou, M Liu, M Hasegawa-Johnson, TS Huang 2008 IEEE Conference on Computer Vision and Pattern Recognition, 1-8, 2008	207	2008
Signal-based and expectation-based factors in the perception of prosodic prominence J Cole, Y Mo, M Hasegawa-Johnson Laboratory Phonology 1 (2), 425-452, 2010	205	2010
Autovc: Zero-shot voice style transfer with only autoencoder loss K Qian, Y Zhang, S Chang, X Yang, M Hasegawa-Johnson International Conference on Machine Learning, 5210-5219, 2019	201	2019
Acoustic fall detection using Gaussian mixture models and GMM supervectors X Zhuang, J Huang, G Potamianos, M Hasegawa-Johnson 2009 IEEE International Conference on Acoustics, Speech and Signal …, 2009	177	2009
Articulatory feature-based methods for acoustic and audio-visual speech recognition: Summary from the 2006 JHU summer workshop K Livescu, O Cetin, M Hasegawa-Johnson, S King, C Bartels, N Borges, ... 2007 IEEE International Conference on Acoustics, Speech and Signal …, 2007	156	2007
Singing-Voice Separation from Monaural Recordings using Deep Recurrent Neural Networks. PS Huang, M Kim, M Hasegawa-Johnson, P Smaragdis ISMIR, 477-482, 2014	155	2014
Sift-bag kernel for video event analysis X Zhou, X Zhuang, S Yan, SF Chang, M Hasegawa-Johnson, TS Huang Proceedings of the 16th ACM international conference on Multimedia, 229-238, 2008	141	2008
Prosodic effects on acoustic cues to stop voicing and place of articulation: Evidence from Radio News speech J Cole, H Kim, H Choi, M Hasegawa-Johnson Journal of Phonetics 35 (2), 180-209, 2007	136	2007
Landmark-based speech recognition: Report of the 2004 Johns Hopkins summer workshop M Hasegawa-Johnson, J Baker, S Borys, K Chen, E Coogan, ... Proceedings.(ICASSP'05). IEEE International Conference on Acoustics, Speech …, 2005	135	2005

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