| Sparse autoencoder-based feature transfer learning for speech emotion recognition J Deng, Z Zhang, E Marchi, B Schuller 2013 humaine association conference on affective computing and intelligent …, 2013 | 492 | 2013 |
| Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments Z Zhang, J Geiger, J Pohjalainen, AED Mousa, W Jin, B Schuller ACM Transactions on Intelligent Systems and Technology (TIST), 28 pages, 2018 | 428 | 2018 |
| Autoencoder-based Unsupervised Domain Adaptation for Speech Emotion Recognition J Deng, Z Zhang, F Eyben, B Schuller IEEE Signal Processing Letters 21 (9), 1068 - 1072, 2014 | 334 | 2014 |
| Semisupervised autoencoders for speech emotion recognition J Deng, X Xu, Z Zhang, S Fruhholz, B Schuller IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP) 26 (1 …, 2018 | 173 | 2018 |
| Cooperative Learning and its Application to Emotion Recognition from Speech Z Zhang, E Coutinho, J Deng, B Schuller IEEE Transactions on Audio, Speech and Language Processing 23 (1), 115-126, 2015 | 164 | 2015 |
| Unsupervised learning in cross-corpus acoustic emotion recognition Z Zhang, F Weninger, M Wöllmer, B Schuller 2011 IEEE Workshop on Automatic Speech Recognition & Understanding, 523-528, 2011 | 150 | 2011 |
| Deep Scalogram Representations for Acoustic Scene Classification Z Ren, K Qian, Z Zhang, V Pandit, A Baird, B Schuller IEEE/CAA Journal of Automatica Sinica, 2018 | 147 | 2018 |
| Universum autoencoder-based domain adaptation for speech emotion recognition J Deng, X Xu, Z Zhang, S Frühholz, B Schuller IEEE Signal Processing Letters 24 (4), 500-504, 2017 | 145 | 2017 |
| An Early Study on Intelligent Analysis of Speech under COVID-19: Severity, Sleep Quality, Fatigue, and Anxiety J Han, K Qian, M Song, Z Yang, Z Ren, S Liu, J Liu, H Zheng, W Ji, ... INTERSPEECH, 2020 | 134 | 2020 |
| Exploring Deep Spectrum Representations via Attention-based Recurrent and Convolutional Neural Networks for Speech Emotion Recognition Z Zhao, Z Bao, Y Zhao, Z Zhang, N Cummins, J Dineley, B Schuller IEEE Access, 2019 | 134 | 2019 |
| From Hard to Soft: Towards more Human-like Emotion Recognition by Modelling the Perception Uncertainty J Han, Z Zhang, M Schmitt, B Schuller Proc. of ACM International Conference on Multimedia (MM), 2017 | 120 | 2017 |
| Attention-Augmented End-to-End Multi-Task Learning for Emotion Prediction from Speech Z Zhang, B Wu, B Schuller Proc. IEEE International Conference on Acoustics, Speech and Signal …, 2019 | 111 | 2019 |
| Combining a parallel 2D CNN with a self-attention Dilated Residual Network for CTC-based discrete speech emotion recognition Z Zhao, Q Li, Z Zhang, N Cummins, H Wang, J Tao, BW Schuller Neural Networks 141, 52-60, 2021 | 101 | 2021 |
| Using multiple databases for training in emotion recognition: To unite or to vote? B Schuller, Z Zhang, F Weninger, G Rigoll Twelfth Annual Conference of the International Speech Communication Association, 2011 | 101 | 2011 |
| ENHANCED SEMI-SUPERVISED LEARNING FOR MULTIMODAL EMOTION RECOGNITION Z Zhang, F Ringeval, B Dong, E Coutinho, E Marchi, B Schuller Proc. of ICASSP, 2016 | 99 | 2016 |
| Robust Speech Recognition using Long Short-Term Memory Recurrent Neural Networks for Hybrid Acoustic Modelling JT Geiger, Z Zhang, F Weninger, B Schuller, G Rigoll INTERSPEECH, 2014 | 98 | 2014 |
| Introducing Shared-Hidden-Layer Autoencoders for Transfer Learning and their Application in Acoustic Emotion Recognition J Deng, R Xia, Z Zhang, Y Liu, B Schuller Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International …, 2014 | 98 | 2014 |
| Exploring Spatio-Temporal Representations by Integrating Attention-based Bidirectional-LSTM-RNNs and FCNs for Speech Emotion Recognition Z Zhao, Y Zheng, Z Zhang, H Wang, Y Zhao, C Li Proc. INTERSPEECH, 2018 | 94 | 2018 |
| Artificial intelligence internet of things for the elderly: From assisted living to health-care monitoring K Qian, Z Zhang, Y Yamamoto, BW Schuller IEEE Signal Processing Magazine 38 (4), 78-88, 2021 | 93 | 2021 |
| Automatic assessment of depression from speech via a hierarchical attention transfer network and attention autoencoders Z Zhao, Z Bao, Z Zhang, J Deng, N Cummins, H Wang, J Tao, B Schuller IEEE Journal of Selected Topics in Signal Processing 14 (2), 423-434, 2019 | 93 | 2019 |
Björn SchullerProfessor, Technische Universität München (TUM) / Imperial College London & CSO, audEERINGVerified email at tum.de