| A review of depression and suicide risk assessment using speech analysis N Cummins, S Scherer, J Krajewski, S Schnieder, J Epps, TF Quatieri Speech Communication 71, 10-49, 2015 | 363 | 2015 |
| Avec 2017: Real-life depression, and affect recognition workshop and challenge F Ringeval, B Schuller, M Valstar, J Gratch, R Cowie, S Scherer, S Mozgai, ... Proceedings of the 7th Annual Workshop on Audio/Visual Emotion Challenge, 3-9, 2017 | 180 | 2017 |
| Snore sound classification using image-based deep spectrum features S Amiriparian, M Gerczuk, S Ottl, N Cummins, M Freitag, S Pugachevskiy, ... | 149 | 2017 |
| An investigation of depressed speech detection: Features and normalization N Cummins, J Epps, M Breakspear, R Goecke Twelfth Annual Conference of the International Speech Communication Association, 2011 | 120 | 2011 |
| Diagnosis of depression by behavioural signals: a multimodal approach N Cummins, J Joshi, A Dhall, V Sethu, R Goecke, J Epps Proceedings of the 3rd ACM international workshop on Audio/visual emotion …, 2013 | 89 | 2013 |
| AVEC 2018 workshop and challenge: Bipolar disorder and cross-cultural affect recognition F Ringeval, B Schuller, M Valstar, R Cowie, H Kaya, M Schmitt, ... Proceedings of the 2018 on audio/visual emotion challenge and workshop, 3-13, 2018 | 82 | 2018 |
| An image-based deep spectrum feature representation for the recognition of emotional speech N Cummins, S Amiriparian, G Hagerer, A Batliner, S Steidl, BW Schuller Proceedings of the 25th ACM international conference on Multimedia, 478-484, 2017 | 82 | 2017 |
| audeep: Unsupervised learning of representations from audio with deep recurrent neural networks M Freitag, S Amiriparian, S Pugachevskiy, N Cummins, B Schuller The Journal of Machine Learning Research 18 (1), 6340-6344, 2017 | 77 | 2017 |
| Sequence to sequence autoencoders for unsupervised representation learning from audio S Amiriparian, M Freitag, N Cummins, B Schuller Universität Augsburg, 2017 | 68 | 2017 |
| An investigation of annotation delay compensation and output-associative fusion for multimodal continuous emotion prediction Z Huang, T Dang, N Cummins, B Stasak, P Le, V Sethu, J Epps Proceedings of the 5th International Workshop on Audio/Visual Emotion …, 2015 | 66 | 2015 |
| Analysis of acoustic space variability in speech affected by depression N Cummins, V Sethu, J Epps, S Schnieder, J Krajewski Speech Communication 75, 27-49, 2015 | 57 | 2015 |
| AVEC 2019 workshop and challenge: state-of-mind, detecting depression with AI, and cross-cultural affect recognition F Ringeval, B Schuller, M Valstar, N Cummins, R Cowie, L Tavabi, ... Proceedings of the 9th International on Audio/Visual Emotion Challenge and …, 2019 | 55 | 2019 |
| Advanced data exploitation in speech analysis: An overview Z Zhang, N Cummins, B Schuller IEEE Signal Processing Magazine 34 (4), 107-129, 2017 | 46 | 2017 |
| Modeling spectral variability for the classification of depressed speech. N Cummins, J Epps, V Sethu, M Breakspear, R Goecke Interspeech, 857-861, 2013 | 46 | 2013 |
| Spectro-temporal analysis of speech affected by depression and psychomotor retardation N Cummins, J Epps, E Ambikairajah 2013 IEEE International Conference on Acoustics, Speech and Signal …, 2013 | 43 | 2013 |
| Speech analysis for health: Current state-of-the-art and the increasing impact of deep learning N Cummins, A Baird, BW Schuller Methods 151, 41-54, 2018 | 41 | 2018 |
| Variability compensation in small data: Oversampled extraction of i-vectors for the classification of depressed speech N Cummins, J Epps, V Sethu, J Krajewski 2014 IEEE International Conference on Acoustics, Speech and Signal …, 2014 | 38 | 2014 |
| The use of technology in suicide prevention ME Larsen, N Cummins, TW Boonstra, B O'Dea, J Tighe, J Nicholas, ... 2015 37th annual international conference of the IEEE engineering in …, 2015 | 36 | 2015 |
| Adversarial training in affective computing and sentiment analysis: Recent advances and perspectives J Han, Z Zhang, N Cummins, B Schuller IEEE Computational Intelligence Magazine 14 (2), 68-81, 2019 | 35 | 2019 |
| Strength modelling for real-worldautomatic continuous affect recognition from audiovisual signals J Han, Z Zhang, N Cummins, F Ringeval, B Schuller Image and Vision Computing 65, 76-86, 2017 | 35 | 2017 |
Björn SchullerProfessor of AI, Imperial College London & University of Augsburg & CSO, audEERINGVahvistettu sähköpostiosoite verkkotunnuksessa imperial.ac.uk
