C. Chang, C. Chang, J. Zheng, and P. Chung, Physiological emotion analysis using support vector regression, Neurocomputing, vol.122, pp.79-87, 2013.
DOI : 10.1016/j.neucom.2013.02.041

L. Chao, J. Tao, M. Yang, Y. Li, and Z. Wen, Long Short Term Memory Recurrent Neural Network based Multimodal Dimensional Emotion Recognition, Proceedings of the 5th International Workshop on Audio/Visual Emotion Challenge, AVEC '15, pp.65-72, 2015.
DOI : 10.1145/2808196.2811634

J. Cohen, P. Cohen, S. G. West, and L. S. Aiken, Applied multiple regression/correlation analysis for the behavioral sciences, 2013.

F. Eyben, M. Wöllmer, and B. Schuller, openSMILE ? the Munich versatile and fast open-source audio feature extractor, Proc. ACM International Conference on Multimedia (ACM MM, pp.1459-1462, 2010.

R. Fan, K. Chang, C. Hsieh, X. Wang, and C. Lin, LIBLINEAR: A library for large linear classification, Journal of Machine Learning Research, vol.9, pp.1871-1874, 2008.

A. Graves and J. Schmidhuber, Framewise phoneme classification with bidirectional LSTM and other neural network architectures, Neural Networks, vol.18, issue.5-6, pp.602-610, 2005.
DOI : 10.1016/j.neunet.2005.06.042
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.331.5800

H. Gunes and M. Pantic, Automatic, Dimensional and Continuous Emotion Recognition, International Journal of Synthetic Emotions, vol.1, issue.1, pp.68-99, 2010.
DOI : 10.4018/jse.2010101605
URL : https://opus.lib.uts.edu.au/bitstream/10453/15170/1/2010001259OK.pdf

H. Gunes and M. Piccardi, Automatic Temporal Segment Detection and Affect Recognition From Face and Body Display, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol.39, issue.1, pp.64-84, 2009.
DOI : 10.1109/TSMCB.2008.927269

H. Gunes and B. Schuller, Categorical and dimensional affect analysis in continuous input: Current trends and future directions, Image and Vision Computing, vol.31, issue.2, pp.120-136, 2013.
DOI : 10.1016/j.imavis.2012.06.016

S. R. Gunn, Support vector machines for classification and regression, 1998.

L. He, D. Jiang, L. Yang, E. Pei, P. Wu et al., Multimodal Affective Dimension Prediction Using Deep Bidirectional Long Short-Term Memory Recurrent Neural Networks, Proceedings of the 5th International Workshop on Audio/Visual Emotion Challenge, AVEC '15, pp.73-80, 2015.
DOI : 10.1145/2808196.2811641

H. Hermansky, D. P. Ellis, and S. Sharma, Tandem connectionist feature extraction for conventional HMM systems, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100), 2000.
DOI : 10.1109/ICASSP.2000.862024
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.29.1232

S. Hochreiter and J. Schmidhuber, Long Short-Term Memory, Neural Computation, vol.4, issue.8, pp.1735-1780, 1997.
DOI : 10.1016/0893-6080(88)90007-X

Z. Huang, T. Dang, N. Cummins, B. Stasak, P. Le et al., An investigation of annotation delay compensation and outputassociative fusion for multimodal continuous emotion prediction, Proc. the 5th International Workshop on Audio/Visual Emotion Challenge (AVEC), pp.41-48, 2015.

N. Kumar, R. Gupta, T. Guha, C. Vaz, M. Van-segbroeck et al., Affective feature design and predicting continuous affective dimensions from music, Proc. MediaEval, 2014.

O. Kursun, H. Seker, F. Grgen, N. Aydin, O. V. Favorov et al., Parallel interacting multiview learning: An application to prediction of protein sub-nuclear location, 2009 9th International Conference on Information Technology and Applications in Biomedicine, pp.1-4, 2009.
DOI : 10.1109/ITAB.2009.5394395

A. Manandhar, K. D. Morton, P. A. Torrione, and L. M. Collins, Multivariate output-associative RVM for multi-dimensional affect predictions, World Academy of Science, Engineering and Technology International Journal of Computer, Electrical, Automation, Control and Information Engineering, vol.10, issue.3, pp.408-415, 2016.

S. Mariooryad and C. Busso, Correcting Time-Continuous Emotional Labels by Modeling the Reaction Lag of Evaluators, IEEE Transactions on Affective Computing, vol.6, issue.2, pp.97-108, 2015.
DOI : 10.1109/TAFFC.2014.2334294

G. Mckeown, M. Valstar, R. Cowie, M. Pantic, and M. Schroder, The SEMAINE Database: Annotated Multimodal Records of Emotionally Colored Conversations between a Person and a Limited Agent, IEEE Transactions on Affective Computing, vol.3, issue.1, pp.5-17, 2012.
DOI : 10.1109/T-AFFC.2011.20

M. A. Nicolaou, H. Gunes, and M. Pantic, Continuous Prediction of Spontaneous Affect from Multiple Cues and Modalities in Valence-Arousal Space, IEEE Transactions on Affective Computing, vol.2, issue.2, pp.92-105, 2011.
DOI : 10.1109/T-AFFC.2011.9

M. A. Nicolaou, H. Gunes, and M. Pantic, Output-associative RVM regression for dimensional and continuous emotion prediction, Image and Vision Computing, vol.30, issue.3, pp.186-196, 2012.
DOI : 10.1016/j.imavis.2011.12.005

M. Pantic and L. J. Rothkrantz, Toward an affect-sensitive multimodal human-computer interaction, Proceedings of the IEEE, vol.91, issue.9, pp.1370-1390, 2003.
DOI : 10.1109/JPROC.2003.817122
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.3.2728

S. Petridis and M. Pantic, Prediction-based audiovisual fusion for classification of non-linguistic vocalisations, IEEE Transactions on Affective Computing, vol.7, issue.1, pp.45-58, 2016.

X. Qiu, L. Zhang, Y. Ren, P. N. Suganthan, and G. Amaratunga, Ensemble deep learning for regression and time series forecasting, 2014 IEEE Symposium on Computational Intelligence in Ensemble Learning (CIEL), pp.1-6, 2014.
DOI : 10.1109/CIEL.2014.7015739

F. Ringeval, F. Eyben, E. Kroupi, A. Yuce, J. Thiran et al., Prediction of asynchronous dimensional emotion ratings from audiovisual and physiological data, Pattern Recognition Letters, vol.66, pp.22-30, 2015.
DOI : 10.1016/j.patrec.2014.11.007

F. Ringeval, B. Schuller, M. Valstar, S. Jaiswal, E. Marchi et al., AV+EC 2015, Proceedings of the 5th International Workshop on Audio/Visual Emotion Challenge, AVEC '15, pp.3-8, 2015.
DOI : 10.1145/2808196.2811642

F. Ringeval, A. Sonderegger, J. Sauer, and D. Lalanne, Introducing the RECOLA multimodal corpus of remote collaborative and affective interactions, 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), pp.1-8, 2013.
DOI : 10.1109/FG.2013.6553805

B. Schuller, S. Reiter, R. Muller, M. Al-hames, M. Lang et al., Speaker Independent Speech Emotion Recognition by Ensemble Classification, 2005 IEEE International Conference on Multimedia and Expo, pp.864-867, 2005.
DOI : 10.1109/ICME.2005.1521560
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.331.2366

B. Schuller, G. Rigoll, and M. Lang, Hidden markov model-based speech emotion recognition, Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp.401-404, 2003.
DOI : 10.1109/icme.2003.1220939
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.364.3782

B. Schuller, M. Valster, F. Eyben, R. Cowie, and M. Pantic, AVEC 2012, Proceedings of the 14th ACM international conference on Multimodal interaction, ICMI '12, pp.449-456, 2012.
DOI : 10.1145/2388676.2388776

M. Soleymani, A. Aljanaki, Y. Yang, M. N. Caro, F. Eyben et al., Emotional Analysis of Music, Proceedings of the ACM International Conference on Multimedia, MM '14, pp.1161-1164, 2014.
DOI : 10.1145/2647868.2655019

M. Soleymani, S. Asghari-esfeden, Y. Fu, and M. Pantic, Analysis of EEG Signals and Facial Expressions for Continuous Emotion Detection, IEEE Transactions on Affective Computing, vol.7, issue.1, pp.17-28, 2016.
DOI : 10.1109/TAFFC.2015.2436926

M. Soleymani, S. Asghari-esfeden, M. Pantic, and Y. Fu, Continuous emotion detection using EEG signals and facial expressions, 2014 IEEE International Conference on Multimedia and Expo (ICME), pp.1-6, 2014.
DOI : 10.1109/ICME.2014.6890301
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.649.3590

L. Tian, J. D. Moore, and C. Lai, Emotion recognition in spontaneous and acted dialogues, 2015 International Conference on Affective Computing and Intelligent Interaction (ACII), pp.698-704, 2015.
DOI : 10.1109/ACII.2015.7344645

M. F. Valstar, J. Gratch, B. W. Schuller, F. Ringeval, D. Lalanne et al., AVEC 2016 -depression, mood, and emotion recognition workshop and challenge, Proc. the 6th International Workshop on Audio/Visual Emotion Challenge, pp.3-10, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01494127

J. Wei, E. Pei, D. Jiang, H. Sahli, L. Xie et al., Multimodal continuous affect recognition based on LSTM and multiple kernel learning, Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific, pp.1-4, 2014.
DOI : 10.1109/APSIPA.2014.7041743

F. Weninger, J. Bergmann, and B. Schuller, Introducing CUR- RENNT: The munich open-source cuda recurrent neural network toolkit, Journal of Machine Learning Research, vol.16, issue.1, pp.547-551, 2015.

F. Weninger, F. Ringeval, E. Marchi, and B. Schuller, Discriminatively trained recurrent neural networks for continuous dimensional emotion recognition from audio, Proc. International Joint Conference on Artificial Intelligence (IJCAI), pp.2196-2202, 2016.

M. Wöllmer, F. Eyben, A. Graves, B. Schuller, and G. Rigoll, Bidirectional LSTM Networks for Context-Sensitive Keyword Detection in a Cognitive Virtual Agent Framework, Cognitive Computation, vol.73, issue.1, pp.180-190, 2010.
DOI : 10.1007/s12559-010-9041-8

M. Wöllmer, F. Eyben, S. Reiter, B. Schuller, C. Cox et al., Abandoning emotion classes-towards continuous emotion recognition with modelling of long-range dependencies, Proc. INTERSPEECH. Brisbane, Australia, pp.597-600, 2008.

M. Wöllmer, F. Eyben, B. Schuller, Y. Sun, T. Moosmayr et al., Robust in-car spelling recognition-a tandem BLSTM- HMM approach, Proc. INTERSPEECH, pp.2507-2510, 2009.

M. Wöllmer, M. Kaiser, F. Eyben, B. Schuller, and G. Rigoll, LSTM-Modeling of continuous emotions in an audiovisual affect recognition framework, Image and Vision Computing, vol.31, issue.2, pp.153-163, 2013.
DOI : 10.1016/j.imavis.2012.03.001

D. H. Wolpert, Stacked generalization, Neural Networks, vol.5, issue.2, pp.241-259, 1992.
DOI : 10.1016/S0893-6080(05)80023-1
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.133.8090

C. Wu, J. Lin, and W. Wei, Survey on audiovisual emotion recognition: databases, features, and data fusion strategies, APSIPA Transactions on Signal and Information Processing, vol.3, pp.12-18, 2014.
DOI : 10.1109/TASL.2007.911513
URL : http://doi.org/10.1017/atsip.2014.11

Y. H. Yang, Y. C. Lin, Y. F. Su, and H. H. Chen, A Regression Approach to Music Emotion Recognition, IEEE Transactions on Audio, Speech, and Language Processing, vol.16, issue.2, pp.448-457, 2008.
DOI : 10.1109/TASL.2007.911513
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.331.1655

Z. Zeng, M. Pantic, G. I. Roisman, and T. S. Huang, A survey of affect recognition methods, Proceedings of the ninth international conference on Multimodal interfaces , ICMI '07, pp.39-58, 2009.
DOI : 10.1145/1322192.1322216

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, issue.3, pp.525-533, 2014.
DOI : 10.1109/TCE.2014.6937339

Z. Zhang, F. Ringeval, B. Dong, E. Coutinho, E. Marchi et al., Enhanced semi-supervised learning for multimodal emotion recognition, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.5185-5189, 2016.
DOI : 10.1109/ICASSP.2016.7472666

Z. Zhang, F. Ringeval, J. Han, J. Deng, E. Marchi et al., Facing Realism in Spontaneous Emotion Recognition from Speech: Feature Enhancement by Autoencoder with LSTM Neural Networks, Interspeech 2016, pp.3593-3597, 2016.
DOI : 10.21437/Interspeech.2016-998
URL : https://hal.archives-ouvertes.fr/hal-01494003