1) Z. Zhang, Semi-Autonomous Data Enrichment and Optimisation for Intelligent Speech Analysis. Munich, Germany: Verlag Dr. Hut, 2015
36) J. Han, Z. Zhang, M. Pantic, and B. Schuller, “Internet of emotional people: Towards continual affective computing cross cultures via audio-visual signals,” Future Generation Computer Systems, 2020. 13 pages,in print (IF: 6.125 (2019))
35) K. Qian, C. Janott, M. Schmitt, Z. Zhang, C. Heiser, W. Hemmert, Y. Yamamoto, and B. W. Schuller, “Can machine learning assist locating the excitation of snore sound? a review,” IEEE Journal of Biomedical and Health Informatics, 2020. 14 pages, in print (IF: 5.223 (2019))
34) H. Zhao, Y. Xiao, and Z. Zhang, “Robust semi-supervised generative adversarial networks for speech emotion recognition via distribution smoothness,” IEEE Access, vol. 8, pp. 106889 – 106900, June 2020. (IF: 3.745 (2019))
33) Z. Zhao, Z. Bao, Z. Zhang, J. Deng, N. Cummins, H. Wang, J. Tao, and B. Schuller, “Automatic assessment of depression from speech via a hierarchical attention transfer network and attention autoencoders,” IEEE Journal of Selected Topics in Signal Processing, 2020. in print (IF: 6.688 (2018))
32) J. Han, Z. Zhang, Z. Ren, and B. Schuller, “Exploring perception uncertainty for emotion recognition in dyadic conversation and music listening,” Cognitive Computation, 2020. 10 pages, in print (IF: 4.287 (2018))
31) Z. Zhang, J. Han, K. Qian, C. Janott, Y. Guo, and B. Schuller, “Snore-GANs: Improving Automatic Snore Sound Classification with Synthesized Data,” IEEE Journal of Biomedical and Health Informatics, vol. 23, 2019. 11 pages, in print (IF: 4.217 (2018))
30) J. Han, Z. Zhang, Z. Ren, and B. Schuller, “Emobed: Strengthening emotion recognition via training with crossmodal emotion embeddings,” IEEE Transactions on Affective Computing, 2019. in print (IF: 6.288 (2018))
29) Z. Zhao, Z. Bao, Y. Zhao, Z. Zhang, N. Cummins, J. Dineley, and B. Schuller, “Exploring Deep Spectrum Representations via Attentionbased Recurrent and Convolutional Neural Networks for Speech Emotion Recognition,” IEEE Access, vol. 7, pp. 97515–97525, 2019. (IF: 4.098 (2018))
28) J. Deng, B. Schuller, F. Eyben, D. Schuller, Z. Zhang, H. Francois, and E. Oh, “Exploiting time-frequency patterns with LSTM RNNs for lowbitrate audio restoration,” Neural Computing and Applications, Special Issue on Deep Learning for Music and Audio, vol. 31, 2019. 13 pages, (IF: 4.664 (2018))
27) K. Qian, M. Schmitt, C. Janott, Z. Zhang, C. Heiser, W. Hohenhorst, M. Herzog, W. Hemmert, and B. Schuller, “Bag of wavelet features for snore sound classification,” Annals of Biomedical Engineering, vol. 47, pp. 1000–1011, Aril 2019. (IF: 3.474 (2018))
26) Z. Zhang, J. Han, E. Coutinho, and B. Schuller, “Dynamic Difficulty Awareness Training for Continuous Emotion Prediction,” IEEE Transactions on Multimedia, vol. 21, pp. 1289–1301, May 2018. (IF: 5.452 (2018))
25) Z. Zhang, J. T. Geiger, J. Pohjalainen, A. E. Mousa, W. Jin, and B. Schuller, “Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments,” ACM Transactions on Intelligent Systems and Technology, vol. 9, June 2018. 14 pages (IF: 2.861 (2018))
24) Z. Zhang, J. Han, J. Deng, X. Xu, F. Ringeval, and B. Schuller, “Leveraging Unlabelled Data for Emotion Recognition with Enhanced Collaborative Semi-Supervised Learning,” IEEE Access, vol. 6, pp. 22196– 22209, April 2018. (IF: 4.098 (2018))
23) J. Han, Z. Zhang*, N. Cummins, and B. Schuller, “Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and Prospectives,” IEEE Computational Intelligence Magazine, Special Issue on Computational Intelligence for Affective Computing and Sentiment Analysis, vol. 14, pp. 68–81, May 2018. (IF: 5.857 (2018))
22) J. Deng, X. Xu, Z. Zhang, S. Fruhholz, and B. Schuller, “Semi- ¨ Supervised Autoencoders for Speech Emotion Recognition,” IEEE/ACM Transactions on Audio, Speech and Language Processing, vol. 26, pp. 31–43, January 2018. (IF: 3.531 (2018))
21) C. Janott, M. Schmitt, Y. Zhang, K. Qian, V. Pandit, Z. Zhang, C. Heiser, W. Hohenhorst, M. Herzog, W. Hemmert, and B. Schuller, “Snoring Classified: The Munich Passau Snore Sound Corpus,” Computers in Biology and Medicine, vol. 94, pp. 106–118, March 2018. (IF: 2.286 (2018))
20) K. Qian, C. Janott, Z. Zhang, J. Deng, A. Baird, C. Heiser, W. Hohenhorst, M. Herzog, W. Hemmer, and B. Schuller, “Teaching Machines on Snoring: A Benchmark on Computer Audition for Snore Sound Excitation Localisation,” Archives of Acoustics, vol. 43, pp. 465–475, May 2018. (IF: 0.899 (2018))
19) J. Han, Z. Zhang, G. Keren, and B. Schuller, “Emotion Recognition in Speech with Latent Discriminative Representations Learning,” Acta Acustica united with Acustica, vol. 104, pp. 737–740, September 2018. (IF: 1.037 (2018))
18) Z. Ren, K. Qian, Z. Zhang, V. Pandit, A. Baird, and B. Schuller, “Deep Scalogram Representations for Acoustic Scene Classification,” IEEE/CAA Journal of Automatica Sinica, vol. 5, pp. 662–669, May 2018. invited contribution
17) Z. Zhang, N. Cummins, and B. Schuller, “Advanced Data Exploitation in Speech Analysis – An Overview,” IEEE Signal Processing Magazine, vol. 34, pp. 107–129, July 2017. (IF: 7.602 (2018))
16) K. Qian, Z. Zhang, A. Baird, and B. Schuller, “Active Learning for Bird Sounds Classification,” Acta Acustica united with Acustica, vol. 103, pp. 361–364, April 2017. (IF: 1.037 (2018))
15) X. Xu, J. Deng, N. Cummins, Z. Zhang, C. Wu, L. Zhao, and B. Schuller, “A Two-Dimensional Framework of Multiple Kernel Subspace Learning for Recognising Emotion in Speech,” IEEE/ACM Transactions on Audio, Speech and Language Processing, vol. 25, pp. 1436–1449, July 2017. (IF: 3.531 (2018))
14) J. Deng, X. Xu, Z. Zhang, S. Fruhholz, and B. Schuller, “Universum Autoencoder-based Domain Adaptation for Speech Emotion Recognition,” IEEE Signal Processing Letters, vol. 24, pp. 500–504, April 2017. (IF: 3.268 (2018))
13) K. Qian, Z. Zhang, A. Baird, and B. Schuller, “Active Learning for Bird Sound Classification via a Kernel-based Extreme Learning Machine,” Journal of the Acoustical Society of America, vol. 142, pp. 1796–1804, October 2017. (IF: 1.819 (2018))
12) K. Qian, C. Janott, V. Pandit, Z. Zhang, C. Heiser, W. Hohenhorst, M. Herzog, W. Hemmert, and B. Schuller, “Classification of the Excitation Location of Snore Sounds in the Upper Airway by Acoustic Multi-Feature Analysis,” IEEE Transactions on Biomedical Engineering, vol. 64, pp. 1731–1741, August 2017. (IF: 4.491 (2017))
11) J. Han, Z. Zhang*, N. Cummins, F. Ringeval, and B. Schuller, “Strength Modelling for Real-World Automatic Continuous Affect Recognition from Audiovisual Signals,” Image and Vision Computing, Special Issue on Multimodal Sentiment Analysis and Mining in the Wild, vol. 65, pp. 76–86, September 2017. (IF: 2.747 (2018))
10) J. Deng, S. Fruhholz, Z. Zhang, and B. Schuller, “Recognizing Emotions ¨ From Whispered Speech Based on Acoustic Feature Transfer Learning,” IEEE Access, vol. 5, pp. 5235–5246, December 2017. (IF: 4.098 (2018))
9) J. Deng, X. Xu, Z. Zhang, S. Fruhholz, and B. Schuller, “Exploitation ¨ of Phase-based Features for Whispered Speech Emotion Recognition,” IEEE Access, vol. 4, pp. 4299–4309, July 2016. (IF: 4.098 (2018))
8) Z. Zhang, E. Coutinho, J. Deng, and B. Schuller, “Cooperative Learning and its Application to Emotion Recognition from Speech,” IEEE/ACM Transactions on Audio, Speech and Language Processing, vol. 23, pp. 115–126, January 2015. (IF: 3.531 (2018))
7) Z. Zhang, E. Coutinho, J. Deng, and B. Schuller, “Distributing Recognition in Computational Paralinguistics,” IEEE Transactions on Affective Computing, vol. 5, pp. 406–417, October–December 2014. (IF: 6.288 (2018))
6) Z. Zhang, J. Pinto, C. Plahl, B. Schuller, and D. Willett, “Channel Mapping using Bidirectional Long Short-Term Memory for Dereverberation in Hands-Free Voice Controlled Devices,” IEEE Transactions on Consumer Electronics, vol. 60, pp. 525–533, August 2014. (acceptance rate: 15 %, IF: 2.083 (2018))
5) J. Deng, Z. Zhang, F. Eyben, and B. Schuller, “Autoencoder-based Unsupervised Domain Adaptation for Speech Emotion Recognition,” IEEE Signal Processing Letters, vol. 21, pp. 1068–1072, September 2014. (IF: 3.268 (2018))
4) B. Schuller, Z. Zhang, F. Weninger, and F. Burkhardt, “Synthesized Speech for Model Training in Cross-Corpus Recognition of Human Emotion,” International Journal of Speech Technology, Special Issue on New and Improved Advances in Speaker Recognition Technologies, vol. 15, pp. 313–323, September 2012
3) W. Zhang, X. Xin, Q. Zhang, Z. Zhang, W. Nai, and Y. Shi, “Centralized light-wave WDM-PON employing DQPSK downstream and OOK remodulated upstream signals,” The Journal of China Universities of Posts and Telecommunications, vol. 17, pp. 125–128, Aug. 2010
2) Z. Long, X. Xin, R. Zhou, Z. Zhang, and D. Xu, “Applications of optical duobinary in optical carrier suppression and separation labeling,” Chinese Optics Letters, vol. 8, pp. 642–646, July 2010. (IF: 1.948 (2017))
1) R. Zhou, X.-J. Xin, Y.-J. Wang, Z.-X. Zhang, and C.-X. Yu, “An Optical Labeling Scheme with Novel DPSK/PPM Orthogonal Modulation,” Chinese Physics Letters, vol. 27, pp. 094209–1–4, Sep. 2010. (IF: 1.066 (2018))
57) J. Han, K. Qian, M. Song, Z. Yang, Z. Ren, S. Liu, J. Liu, H. Zheng, W. Ji, T. Koike, X. Li, Z. Zhang, Y. Yamamoto, and B. W. Schuller, “An early study on intelligent analysis of speech under covid-19: Severity, sleep quality, fatigue, and anxiety,” in Proc. INTERSPEECH, (Shanghai, China), p. 5 pages, 2020
56) Z. Ren, A. Baird, J. Han, Z. Zhang, and B. Schuller, “Generating and protecting against adversarial attacks for deep speech-based emotion recognition models,” in Proc. 45th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), (Barcelona, Spain), p. 5 pages, 2020
55) Z. Zhao, Z. Bao, Z. Zhang, N. Cummins, H. Wang, and B. Schuller, “Automatic assessment of depression from speech based on hierarchical attention transfer network,” in Proc. 45th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), (Barcelona, Spain), p. 5 pages, 2020
54) N. A. Futaisi, Z. Zhang, A. S. Warlaumont, A. Cristia, and B. Schuller, “VCMNet: Weakly supervised learning for automatic infant vocalisation maturity analysis,” in Proc. 21st ACM International Conference on Multimodal Interaction (ICMI), (Suzhou, China), p. 5 pages, 2019
53) M. Song, Z. Yang, A. Baird, E. Parada-Cabaleiro, Z. Zhang, Z. Zhao, and B. Schuller, “Audiovisual analysis for recognising frustration during game-play: Introducing the multimodal game frustration database,” in Proc. 8th biannual Humaine Association Conference on Affective Computing and Intelligent Interaction (ACII), (Cambridge, UK), p. 5 pages, 2019
52) K. Qian, H. Kuromiya, Z. Ren, M. Schmitt, Z. Zhang, T. Nakamura, K. Yoshiuchi, B. Schuller, and Y. Yamamoto, “Automatic detection of major depressive disorder via a bag-of-behaviour-words approach,” in Proc. Third International Symposium on Image Computing and Digital Medicine (ISICDM), (Xi’an, China), p. 5 pages, 2019
51) Z. Yang, K. Qian, Z. Ren, A. Baird, Z. Zhang, and B. Schuller, “Learning multi-resolution representations for acoustic scene classification via neural networks,” in Proc. 7th Conference on Sound and Music Technology (CSMT), (Harbin, China), p. 11 pages, 2019
50) Z. Zhao, Z. Bao, Z. Zhang, N. Cummins, H. Wang, and B. Schuller, “Attention-enhanced connectionist temporal classification for discrete speech emotion recognition,” in Proc. 20th Annual Conference of the International Speech Communication Association (INTERSPEECH), (Graz, Astria), p. 5 pages, 2019
49) X. Xu, J. Deng, N. Cummins, Z. Zhang, L. Zhao, and B. Schuller, “Autonomous emotion learning in speech: A view of zero-shot speech emotion recognition,” in Proc. 20th Annual Conference of the International Speech Communication Association (INTERSPEECH), (Graz, Astria), p. 5 pages, 2019
48) Z. Zhang, B. Wu, and B. Schuller, “Attention-augmented end-to-end multi-task learning for emotion prediction from speech,” in Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (Brighton, UK), 2019
47) J. Han, Z. Zhang, Z. Ren, and B. Schuller, “Implicit fusion by joint audiovisual training for emotion recognition in mono modality,” in Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (Brighton, UK), 2019
46) H. Zhao, Y. Xiao, J. Han, and Z. Zhang, “Compact convolutional recurrent neural networks via binarization for speech emotion recognition,” in Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (Brighton, UK), 2019
45) K. Qian, H. Kuromiya, Z. Zhang, T. Nakamura, K. Yoshiuchi, B. Schuller, and Y. Yamamoto, “Teaching Machines to Know Your Depressive State: On Physical Activity in Health and Major Depressive Disorder,” in Proc. 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Conference (EMBC), (Berlin, Germany), IEEE, July 2019
44) Z. Zhang, A. Cristia, A. S. Warlaumont, and B. Schuller, “Automated classification of children’s linguistic versus non-linguistic vocalisations,” in Proc. 19th Annual Conference of the International Speech Communication Association (INTERSPEECH), (Hyderabad, India), pp. 2588–2592, 2018
43) Z. Zhang, J. Han, K. Qian, and B. Schuller, “Evolving learning for analysing mood-related infant vocalisation,” in Proc. 19th Annual Conference of the International Speech Communication Association (INTERSPEECH), (Hyderabad, India), pp. 142–146, 2018
42) Z. Zhang, A. Warlaumont, B. Schuller, G. Yetish, C. Scaff, H. Colleran, J. Stieglitz, and A. Cristia, “Developing computational measures of vocal maturity from daylong recordings,” in Proc. 16th annual conference of the French Phonology Network (RFP), (Paris, France), 2018
41) J. Han, Z. Zhang, M. Schmitt, Z. Ren, F. Ringeval, and B. Schuller, “Bags in bag: Generating context-aware bags for tracking emotions from speech,” in Proc. 19th Annual Conference of the International Speech Communication Association (INTERSPEECH), (Hyderabad, India), 2018. 3082–3086
40) Z. Zhao, Y. Zhao, Z. Bao, H. Wang, Z. Zhang, and C. Li, “Deep spectrum feature representations for speech emotion recognition,” in Proc. The Joint Workshop of 4th the Workshop on Affective Social Multimedia Computing and first Multi-Modal Affective Computing of Large-Scale Multimedia Data Workshop (ASMMC – MMAC), associate with ACM Multimedia (ACM MM), (Seoul, Korea), pp. 27–33, 2018
39) Y. Guo, J. Han, Z. Zhang, B. Schuller, and Y. Ma, “Exploring a new method for food likability rating based on DT-CWT theory,” in Proc. ACM International Conference on Multimodal Interaction (ICMI), (Boulder, CO, USA), pp. 569–573, 2018
38) Z. Zhao, Y. Zheng, Z. Zhang, H. Wang, Y. Zhao, and C. Li, “Exploring spatio-temporal representations by integrating attention-based bidirectional-LSTM-RNNs and FCNs for speech emotion recognition,” in Proc. 19th Annual Conference of the International Speech Communication Association (INTERSPEECH), (Hyderabad, India), pp. 272–276, 2018
37) J. Han, Z. Zhang, Z. Ren, F. Ringeval, and B. Schuller, “Towards conditional adversarial training for predicting emotions from speech,” in Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (Calgary, Canada), p. 5 pages, IEEE, 2018
36) J. Han, Z. Zhang, M. Schmitt, and B. Schuller, “From Hard to Soft: Towards more Human-like Emotion Recognition by Modelling the Perception Uncertainty,” in Proc. the ACM International Conference on Multimedia (MM), (Mountain View, CA), ACM, ACM, October 2017. (acceptance rate: 28 %)
35) K. Qian, Z. Ren, V. Pandit, Z. Yang, Z. Zhang, and B. Schuller, “Wavelets Revisited for the Classification of Acoustic Scenes,” in Proc. the Detection and Classification of Acoustic Scenes and Events 2017 IEEE AASP Challenge Workshop (DCASE), (Munich, Germany), IEEE, IEEE, November 2017. 5 pages
34) Z. Ren, K. Qian, V. Pandit, Z. Zhang, Z. Yang, and B. Schuller, “Deep Sequential Image Features on Acoustic Scene Classification,” in Proc. the Detection and Classification of Acoustic Scenes and Events 2017 IEEE AASP Challenge Workshop (DCASE), (Munich, Germany), IEEE, IEEE, November 2017. 5 pages
33) S. Hantke, Z. Zhang, and B. Schuller, “Towards Intelligent Crowdsourcing for Audio Data Annotation: Integrating Active Learning in the Real World,” in Proc. 18th Annual Conference of the International Speech Communication Association (INTERSPEECH), (Stockholm, Sweden), pp. 3951–3955, ISCA, ISCA, August 2017
32) Z. Zhang, F. Weninger, M. Wollmer, J. Han, and B. Schuller, “Towards Intoxicated Speech Recognition,” in Proc. 30th International Joint Conference on Neural Networks (IJCNN), (Anchorage, AK), IEEE, IEEE, May 2017. 1555-1559
31) J. Han, Z. Zhang, F. Ringeval, and B. Schuller, “Prediction-based Learning from Continuous Emotion Recognition in Speech,” in Proc. 42nd IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), (New Orleans, LA), pp. 5005–5009, IEEE, IEEE, March 2017
30) J. Han, Z. Zhang, F. Ringeval, and B. Schuller, “Reconstruction-errorbased Learning for Continuous Emotion Recognition in Speech,” in Proc. 42nd IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), (New Orleans, LA), pp. 2367–2371, IEEE, IEEE, March 2017
29) Z. Zhang, F. Ringeval, B. Dong, E. Coutinho, E. Marchi, and B. Schuller, “Enhanced Semi-Supervised Learning for Multimodal Emotion Recognition,” in Proc. 41st IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), (Shanghai, China), pp. 5185–5189, IEEE, IEEE, March 2016
28) Z. Zhang, F. Ringeval, J. Han, J. Deng, E. Marchi, and B. Schuller, “Facing Realism in Spontaneous Emotion Recognition from Speech: Feature Enhancement by Autoencoder with LSTM Neural Networks,” in Proc. 17th Annual Conference of the International Speech Communication Association (INTERSPEECH), (San Francisco, CA), pp. 3593– 3597, ISCA, ISCA, September 2016
27) J. Pohjalainen, F. Ringeval, Z. Zhang, and B. Schuller, “Spectral and Cepstral Audio Noise Reduction Techniques in Speech Emotion Recognition,” in Proc. the 24th ACM International Conference on Multimedia, MM 2016, (Amsterdam, The Netherlands), pp. 670–674, ACM, ACM, October 2016. (acceptance rate short paper: 30 %)
26) J. Deng, X. Xu, Z. Zhang, S. Fruhholz, D. Grandjean, and B. Schuller, ¨ “Fisher Kernels on Phase-based Features for Speech Emotion Recognition,” in Proc. the Seventh International Workshop on Spoken Dialogue Systems (IWSDS), (Saariselka, Finland), Springer, January 2016. 6 pages
25) J. Deng, N. Cummins, J. Han, X. Xu, Z. Ren, V. Pandit, Z. Zhang, and B. Schuller, “The University of Passau Open Emotion Recognition System for the Multimodal Emotion Challenge,” in Proc. the 7th Chinese Conference on Pattern Recognition (CCPR), (Chengdu, P. R. China), pp. 652–666, Springer, November 2016
24) X. Xu, J. Deng, M. Gavryukova, Z. Zhang, L. Zhao, and B. Schuller, “Multiscale Kernel Locally Penalised Discriminant Analysis Exemplified by Emotion Recognition in Speech,” in Proc. the 18th ACM International Conference on Multimodal Interaction (ICMI) (L.-P. Morency, C. Busso, and C. Pelachaud, eds.), (Tokyo, Japan), pp. 233–237, ACM, ACM, November 2016
23) K. Qian, C. Janott, Z. Zhang, C. Heiser, and B. Schuller, “Wavelet Features for Classification of VOTE Snore Sounds,” in Proc. 41st IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), (Shanghai, P. R. China), pp. 221–225, IEEE, IEEE, March 2016
22) B. Dong, Z. Zhang, and B. Schuller, “Empirical Mode Decomposition: A Data-Enrichment Perspective on Speech Emotion Recognition,” in Proc. the 6th International Workshop on Emotion and Sentiment Analysis (ESA 2016), satellite of the 10th Language Resources and Evaluation Conference (LREC) (J. Sanchez-Rada and B. Schuller, eds.), (Portoroz, ´ Slovenia), pp. 71–75, ELRA, ELRA, May 2016
21) Y. Zhang, E. Coutinho, Z. Zhang, C. Quan, and B. Schuller, “Agreement-based Dynamic Active Learning with Least and Medium Certainty Query Strategy,” in Proc. Advances in Active Learning : Bridging Theory and Practice Workshop held in conjunction with the 32nd International Conference on Machine Learning (ICML) (A. Krishnamurthy, A. Ramdas, N. Balcan, and A. Singh, eds.), (Lille, France), International Machine Learning Society, IMLS, July 2015. 5 pages
20) Y. Zhang, E. Coutinho, Z. Zhang, C. Quan, and B. Schuller, “Dynamic Active Learning Based on Agreement and Applied to Emotion Recognition in Spoken Interactions,” in Proc. 17th International Conference on Multimodal Interaction (ICMI), (Seattle, WA), pp. 275–278, ACM, ACM, November 2015
19) Y. Zhang, E. Coutinho, Z. Zhang, M. Adam, and B. Schuller, “On Rater Reliability and Agreement Based Dynamic Active Learning,” in Proc. 6th biannual Conference on Affective Computing and Intelligent Interaction (ACII), (Xi’an, China), pp. 70–76, AAAC, IEEE, September 2015. (acceptance rate oral: 28 %))
18) K. Qian, Z. Zhang, F. Ringeval, and B. Schuller, “Bird Sounds Classification by Large Scale Acoustic Features and Extreme Learning Machine,” in Proc. 3rd IEEE Global Conference on Signal and Information Processing, (GlobalSIP), (Orlando, FL), pp. 1317–1321, IEEE, IEEE, December 2015
17) J. Deng, Z. Zhang, F. Eyben, and B. Schuller, “Autoencoder-based Unsupervised Domain Adaptation for Speech Emotion Recognition,” in Proc. 40th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), (Brisbane, Australia), pp. 1068–1072, IEEE, IEEE, April 2015
16) Z. Zhang, F. Eyben, J. Deng, and B. Schuller, “An Agreement and Sparseness-based Learning Instance Selection and its Application to Subjective Speech Phenomena,” in Proc. the 5th International Workshop on Emotion Social Signals, Sentiment & Linked Open Data (ES3LOD 2014), satellite of the 9th Language Resources and Evaluation Conference (LREC) (B. Schuller, P. Buitelaar, L. Devillers, C. Pelachaud, T. Declerck, A. Batliner, P. Rosso, and S. Gaines, eds.), (Reykjavik, Iceland), pp. 21–26, ELRA, ELRA, May 2014
15) J. Deng, R. Xia, Z. Zhang, Y. Liu, and B. Schuller, “Introducing SharedHidden-Layer Autoencoders for Transfer Learning and their Application in Acoustic Emotion Recognition,” in Proc. 39th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), (Florence, Italy), pp. 4818–4812, IEEE, IEEE, May 2014
14) J. Deng, Z. Zhang, and B. Schuller, “Linked Source and Target Domain Subspace Feature Transfer Learning – Exemplified by Speech Emotion Recognition,” in Proc. 22nd International Conference on Pattern Recognition (ICPR), (Stockholm, Sweden), pp. 761–766, IAPR, IAPR, August 2014
13) J. T. Geiger, Z. Zhang, F. Weninger, B. Schuller, and G. Rigoll, “Robust Speech Recognition using Long Short-Term Memory Recurrent Neural Networks for Hybrid Acoustic Modelling,” in Proc. 15th Annual Conference of the International Speech Communication Association (INTERSPEECH), (Singapore, Singapore), ISCA, ISCA, September 2014
12) Z. Zhang, J. Deng, and B. Schuller, “Co-Training Succeeds in Computational Paralinguistics,” in Proc. 38th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), (Vancouver, Canada), pp. 8505–8509, IEEE, IEEE, May 2013
11) Z. Zhang, J. Deng, E. Marchi, and B. Schuller, “Active Learning by Label Uncertainty for Acoustic Emotion Recognition,” in Proc. 14th Annual Conference of the International Speech Communication Association (INTERSPEECH), (Lyon, France), pp. 2856–2860, ISCA, ISCA, August 2013
10) J. Deng, Z. Zhang, E. Marchi, and B. Schuller, “Sparse Autoencoderbased Feature Transfer Learning for Speech Emotion Recognition,” in Proc. 5th biannual Humaine Association Conference on Affective Computing and Intelligent Interaction (ACII 2013), (Geneva, Switzerland), pp. 511–516, HUMAINE Association, IEEE, September 2013. (acceptance rate oral: 31 %))
9) M. Wollmer, Z. Zhang, F. Weninger, B. Schuller, and G. Rigoll, “Feature ¨ Enhancement by Bidirectional LSTM Networks for Conversational Speech Recognition in Highly Non-Stationary Noise,” in Proc. 38th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), (Vancouver, Canada), pp. 6822–6826, IEEE, IEEE, May 2013
8) Z. Zhang, F. Weninger, and B. Schuller, “Towards Automatic Intoxication Detection from Speech in Real-Life Acoustic Environments,” in Proc. ITG Speech Communication (T. Fingscheidt and W. Kellermann, eds.), (Braunschweig, Germany), pp. 1–4, ITG, IEEE, September 2012. invited contribution
7) Z. Zhang and B. Schuller, “Semi-supervised Learning Helps in Sound Event Classification,” in Proc. 37th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), (Kyoto, Japan), pp. 333–336, IEEE, IEEE, March 2012
6) W. Han, Z. Zhang, J. Deng, M. Wollmer, F. Weninger, and B. Schuller, ¨ “Towards Distributed Recognition of Emotion in Speech,” in Proc. 5th International Symposium on Communications, Control, and Signal Processing (ISCCSP), (Rome, Italy), pp. 1–4, IEEE, IEEE, May 2012. invited contribution
5) Z. Zhang and B. Schuller, “Active Learning by Sparse Instance Tracking and Classifier Confidence in Acoustic Emotion Recognition,” in Proc. 13th Annual Conference of the International Speech Communication Association (INTERSPEECH), (Portland, OR), pp. 362–365, ISCA, ISCA, September 2012
4) B. Schuller, S. Hantke, F. Weninger, W. Han, Z. Zhang, and S. Narayanan, “Automatic Recognition of Emotion Evoked by General Sound Events,” in Proc. 37th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), (Kyoto, Japan), pp. 341–344, IEEE, IEEE, March 2012
3) Z. Zhang, F. Weninger, M. Wollmer, and B. Schuller, “Unsupervised Learning in Cross-Corpus Acoustic Emotion Recognition,” in Proc. 12th Biannual IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), (Big Island, HY), pp. 523–528, IEEE, IEEE, December 2011
2) B. Schuller, Z. Zhang, F. Weninger, and G. Rigoll, “Selecting Training Data for Cross-Corpus Speech Emotion Recognition: Prototypicality vs. Generalization,” in Proc. Speech Processing Conference, (Tel Aviv, Israel), AVIOS, AVIOS, June 2011. invited contribution, 4 pages
1) B. Schuller, Z. Zhang, F. Weninger, and G. Rigoll, “Using Multiple Databases for Training in Emotion Recognition: To Unite or to Vote?,” in Proc. 12th Annual Conference of the International Speech Communication Association (INTERSPEECH), (Florence, Italy), pp. 1553–1556, ISCA, ISCA, August 2011
1) Z. Zhang, D. Liu, J. Han, and B. Schuller, “Learning Audio Sequence Representations for Acoustic Event Classification,” arxiv.org, July 2017. 8 pages