| The OMG-emotion behavior dataset P Barros, N Churamani, E Lakomkin, H Siqueira, A Sutherland, S Wermter 2018 International Joint Conference on Neural Networks (IJCNN), 1-7, 2018 | 75 | 2018 |
| Reusing neural speech representations for auditory emotion recognition E Lakomkin, C Weber, S Magg, S Wermter arXiv preprint arXiv:1803.11508, 2018 | 36 | 2018 |
| On the robustness of speech emotion recognition for human-robot interaction with deep neural networks E Lakomkin, MA Zamani, C Weber, S Magg, S Wermter 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2018 | 32 | 2018 |
| GradAscent at EmoInt-2017: character-and word-level recurrent neural network models for tweet emotion intensity detection E Lakomkin, C Bothe, S Wermter arXiv preprint arXiv:1803.11509, 2018 | 19 | 2018 |
| Emorl: continuous acoustic emotion classification using deep reinforcement learning E Lakomkin, MA Zamani, C Weber, S Magg, S Wermter 2018 IEEE International Conference on Robotics and Automation (ICRA), 4445-4450, 2018 | 17 | 2018 |
| KT-speech-crawler: Automatic dataset construction for speech recognition from YouTube videos E Lakomkin, S Magg, C Weber, S Wermter arXiv preprint arXiv:1903.00216, 2019 | 16 | 2019 |
| Incorporating end-to-end speech recognition models for sentiment analysis E Lakomkin, MA Zamani, C Weber, S Magg, S Wermter 2019 International Conference on Robotics and Automation (ICRA), 7976-7982, 2019 | 14 | 2019 |
| Subword regularization: An analysis of scalability and generalization for end-to-end automatic speech recognition E Lakomkin, J Heymann, I Sklyar, S Wiesler arXiv preprint arXiv:2008.04034, 2020 | 5 | 2020 |
| Анализ статистических алгоритмов снятия морфологической омонимии в русском языке ЕД Лакомкин, ИВ Пузыревский, ДА Рыжова НИУВШЭ, компания Яндекс, М, 2013 | 5 | 2013 |
| Combining articulatory features with end-to-end learning in speech recognition L Qu, C Weber, E Lakomkin, J Twiefel, S Wermter International Conference on Artificial Neural Networks, 500-510, 2018 | 4 | 2018 |
| Predictive Auxiliary Variational Autoencoder for Representation Learning of Global Speech Characteristics. S Springenberg, E Lakomkin, C Weber, S Wermter INTERSPEECH, 934-938, 2019 | 3 | 2019 |
| Automatically augmenting an emotion dataset improves classification using audio E Lakomkin, C Weber, S Wermter EACL 2017, 194, 2017 | 3 | 2017 |
| Image-to-Text Transduction with Spatial Self-Attention. S Springenberg, E Lakomkin, C Weber, S Wermter ESANN, 2018 | 2 | 2018 |
| Методы и практики проектирования web-приложений реального времени с использованием технологии java ЕД Лакомкин RSDN Magazine, 34-38, 2012 | 1 | 2012 |
| Being Greedy Does Not Hurt: Sampling Strategies for End-To-End Speech Recognition J Heymann, E Lakomkin, L Rädel ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and …, 2022 | | 2022 |
| Speech Emotion Recognition for Human-Robot Interaction with Deep Neural Networks E Lakomkin, C Weber, S Magg, S Wermter | | |
| MVC web framework based on eXist application server and XRX architecture♣ YGE Lakomkin, S Ionkin, M Davtyan | | |
Mohammad Ali ZamaniHamburg Informatics Technology Center (HITeC) | University of HamburgVerified email at informatik.uni-hamburg.de
