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Zheng-Hua Tan
Zheng-Hua Tan
Professor of Machine Learning and Speech Processing, Aalborg University
Dirección de correo verificada de es.aau.dk - Página principal
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Permutation invariant training of deep models for speaker-independent multi-talker speech separation
D Yu, M Kolbæk, ZH Tan, J Jensen
2017 IEEE International Conference on Acoustics, Speech and Signal …, 2017
5922017
Multitalker speech separation with utterance-level permutation invariant training of deep recurrent neural networks
M Kolbæk, D Yu, ZH Tan, J Jensen
IEEE/ACM Transactions on Audio, Speech, and Language Processing 25 (10 …, 2017
5612017
Conditional generative adversarial networks for speech enhancement and noise-robust speaker verification
D Michelsanti, ZH Tan
arXiv preprint arXiv:1709.01703, 2017
2072017
Speech intelligibility potential of general and specialized deep neural network based speech enhancement systems
M Kolbæk, ZH Tan, J Jensen
IEEE/ACM Transactions on Audio, Speech, and Language Processing 25 (1), 153-167, 2016
1532016
Reddots replayed: A new replay spoofing attack corpus for text-dependent speaker verification research
T Kinnunen, M Sahidullah, M Falcone, L Costantini, RG Hautamäki, ...
2017 IEEE International conference on acoustics, speech and signal …, 2017
1192017
Decorrelation of neutral vector variables: Theory and applications
Z Ma, JH Xue, A Leijon, ZH Tan, Z Yang, J Guo
IEEE transactions on neural networks and learning systems 29 (1), 129-143, 2016
1132016
Low-complexity variable frame rate analysis for speech recognition and voice activity detection
ZH Tan, B Lindberg
IEEE Journal of Selected Topics in Signal Processing 4 (5), 798-807, 2010
1132010
Automatic speech recognition on mobile devices and over communication networks
ZH Tan, B Lindberg
Springer Science & Business Media, 2008
992008
On loss functions for supervised monaural time-domain speech enhancement
M Kolbæk, ZH Tan, SH Jensen, J Jensen
IEEE/ACM Transactions on Audio, Speech, and Language Processing 28, 825-838, 2020
722020
Adaptive protection combined with machine learning for microgrids
H Lin, K Sun, ZH Tan, C Liu, JM Guerrero, JC Vasquez
IET Generation, Transmission & Distribution 13 (6), 770-779, 2019
662019
Spoofing detection in automatic speaker verification systems using DNN classifiers and dynamic acoustic features
H Yu, ZH Tan, Z Ma, R Martin, J Guo
IEEE transactions on neural networks and learning systems 29 (10), 4633-4644, 2017
642017
Automatic speech recognition over error-prone wireless networks
ZH Tan, P Dalsgaard, B Lindberg
Speech Communication 47 (1-2), 220-242, 2005
602005
An overview of deep-learning-based audio-visual speech enhancement and separation
D Michelsanti, ZH Tan, SX Zhang, Y Xu, M Yu, D Yu, J Jensen
IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2021
552021
rVAD: An unsupervised segment-based robust voice activity detection method
ZH Tan, N Dehak
Computer speech & language 59, 1-21, 2020
552020
Internet of Things: Opportunities and Challenges
ZH Tan, NR Prasad
Tutorial at WPMC2010, Recife, Brazil, 2010
55*2010
Monaural speech enhancement using deep neural networks by maximizing a short-time objective intelligibility measure
M Kolbæk, ZH Tan, J Jensen
2018 IEEE International Conference on Acoustics, Speech and Signal …, 2018
512018
Speech enhancement using long short-term memory based recurrent neural networks for noise robust speaker verification
M Kolboek, ZH Tan, J Jensen
2016 IEEE spoken language technology workshop (SLT), 305-311, 2016
512016
DNN filter bank cepstral coefficients for spoofing detection
H Yu, ZH Tan, Y Zhang, Z Ma, J Guo
Ieee Access 5, 4779-4787, 2017
482017
Integrated spoofing countermeasures and automatic speaker verification: An evaluation on ASVspoof 2015
M Sahidullah, H Delgado, M Todisco, H Yu, T Kinnunen, N Evans, ZH Tan
ISCA (the International Speech Communication Association), 2016
472016
Robust speech recognition based on noise and snr classification-a multiple-model framework
H Xu, ZH Tan, P Dalsgaard, B Lindberg
Ninth European Conference on Speech Communication and Technology, 2005
472005
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