| 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 | 983 | 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 | 397 | 2017 |
| 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 | 347 | 2019 |
| Snore sound classification using image-based deep spectrum features S Amiriparian, M Gerczuk, S Ottl, N Cummins, M Freitag, S Pugachevskiy, ... | 325 | 2017 |
| An investigation of depressed speech detection: Features and normalization N Cummins, J Epps, M Breakspear, R Goecke INTERSPEECH 2011 12th Annual Conference of the International Speech …, 2011 | 233 | 2011 |
| Using smartphones and wearable devices to monitor behavioral changes during COVID-19 S Sun, AA Folarin, Y Ranjan, Z Rashid, P Conde, C Stewart, N Cummins, ... Journal of medical Internet research 22 (9), e19992, 2020 | 226 | 2020 |
| 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 | 201 | 2018 |
| 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 | 189 | 2018 |
| audeep: Unsupervised learning of representations from audio with deep recurrent neural networks M Freitag, S Amiriparian, S Pugachevskiy, N Cummins Journal of Machine Learning Research 18 (173), 1-5, 2018 | 188 | 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 | 186 | 2017 |
| 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 | 166 | 2013 |
| 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, Z Ren, B Schuller IEEE access 7, 97515-97525, 2019 | 130 | 2019 |
| 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 | 120 | 2015 |
| Sequence to sequence autoencoders for unsupervised representation learning from audio S Amiriparian, M Freitag, N Cummins, B Schuller DCASE, 17-21, 2017 | 117 | 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 | 99 | 2015 |
| 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 | 98 | 2021 |
| Emotional expression in psychiatric conditions: New technology for clinicians K Grabowski, A Rynkiewicz, A Lassalle, S Baron‐Cohen, B Schuller, ... Psychiatry and clinical neurosciences 73 (2), 50-62, 2019 | 91 | 2019 |
| 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 | 90 | 2019 |
| 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 | 89 | 2019 |
| Learning image-based representations for heart sound classification Z Ren, N Cummins, V Pandit, J Han, K Qian, B Schuller Proceedings of the 2018 international conference on digital health, 143-147, 2018 | 87 | 2018 |
Björn SchullerProfessor, Technische Universität München (TUM) / Imperial College London & CSO, audEERINGVerifierad e-postadress på tum.de
