| 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 | 1466 | 2017 |
| 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 | 609 | 2019 |
| 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 | 559 | 2014 |
| 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 | 555 | 2015 |
| Brain anatomy differences in childhood stuttering SE Chang, KI Erickson, NG Ambrose, MA Hasegawa-Johnson, ... Neuroimage 39 (3), 1333-1344, 2008 | 470 | 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 | 454 | 2012 |
| Dysarthric speech database for universal access research. H Kim, M Hasegawa-Johnson, A Perlman, JR Gunderson, TS Huang, ... Interspeech 2008, 1741-1744, 2008 | 437 | 2008 |
| 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 | 430 | 2016 |
| 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 | 400 | 2017 |
| AVICAR: audio-visual speech corpus in a car environment. B Lee, M Hasegawa-Johnson, C Goudeseune, S Kamdar, S Borys, M Liu, ... Interspeech, 2489-2492, 2004 | 248 | 2004 |
| 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 | 246 | 2010 |
| Unsupervised speech decomposition via triple information bottleneck K Qian, Y Zhang, S Chang, M Hasegawa-Johnson, D Cox International Conference on Machine Learning, 7836-7846, 2020 | 221 | 2020 |
| Real-world acoustic event detection X Zhuang, X Zhou, MA Hasegawa-Johnson, TS Huang Pattern recognition letters 31 (12), 1543-1551, 2010 | 221 | 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 | 216 | 2008 |
| 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 | 206 | 2009 |
| Singing-Voice Separation from Monaural Recordings using Deep Recurrent Neural Networks. PS Huang, M Kim, M Hasegawa-Johnson, P Smaragdis ISMIR, 477-482, 2014 | 194 | 2014 |
| 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 | 168 | 2007 |
| 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 | 161 | 2007 |
| 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 | 140 | 2008 |
| 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 | 140 | 2005 |
Thomas S. HuangUniversity of Illinois, Urbana-Champaign在 ifp.uiuc.edu 的电子邮件经过验证
