The llama 3 herd of models A Dubey, A Jauhri, A Pandey, A Kadian, A Al-Dahle, A Letman, A Mathur, ... arXiv preprint arXiv:2407.21783, 2024 | 1261 | 2024 |
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 | 136 | 2018 |
Prompting large language models with speech recognition abilities Y Fathullah, C Wu, E Lakomkin, J Jia, Y Shangguan, K Li, J Guo, W Xiong, ... ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and …, 2024 | 95 | 2024 |
The llama 3 herd of models, 2024 A Dubey, A Jauhri, A Pandey, A Kadian, A Al-Dahle, A Letman, A Mathur, ... URL https://arxiv. org/abs/2407.21783 2407, 21783, 0 | 74 | |
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 | 68 | 2018 |
Reusing neural speech representations for auditory emotion recognition E Lakomkin, C Weber, S Magg, S Wermter arXiv preprint arXiv:1803.11508, 2018 | 45 | 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 | 29 | 2018 |
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 | 28 | 2019 |
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 | 25 | 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 | 24 | 2019 |
Synthvsr: Scaling up visual speech recognition with synthetic supervision X Liu, E Lakomkin, K Vougioukas, P Ma, H Chen, R Xie, M Doulaty, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 20 | 2023 |
AudioChatLlama: Towards General-Purpose Speech Abilities for LLMs Y Fathullah, C Wu, E Lakomkin, K Li, J Jia, Y Shangguan, J Mahadeokar, ... Proceedings of the 2024 Conference of the North American Chapter of the …, 2024 | 18 | 2024 |
End-to-end speech recognition contextualization with large language models E Lakomkin, C Wu, Y Fathullah, O Kalinli, ML Seltzer, C Fuegen ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and …, 2024 | 17 | 2024 |
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 | 10 | 2020 |
Combining articulatory features with end-to-end learning in speech recognition L Qu, C Weber, E Lakomkin, J Twiefel, S Wermter Artificial Neural Networks and Machine Learning–ICANN 2018: 27th …, 2018 | 9 | 2018 |
Анализ статистических алгоритмов снятия морфологической омонимии в русском языке ЕД Лакомкин, ИВ Пузыревский, ДА Рыжова URL: http://aistconf. org/stuff/aist2013/submissions/aist2013_submission_33. pdf, 2013 | 6 | 2013 |
Towards general-purpose speech abilities for large language models using unpaired data Y Fathullah, C Wu, E Lakomkin, J Jia, Y Shangguan, J Mahadeokar, ... arXiv preprint arXiv:2311.06753, 2023 | 5 | 2023 |
Automatically augmenting an emotion dataset improves classification using audio E Lakomkin, C Weber, S Wermter EACL 2017, 194, 2017 | 5 | 2017 |
Predictive Auxiliary Variational Autoencoder for Representation Learning of Global Speech Characteristics. S Springenberg, E Lakomkin, C Weber, S Wermter INTERSPEECH, 934-938, 2019 | 4 | 2019 |
On the robustness of speech emotion recognition for human-robot interaction with deep neural networks. In 2018 IEEE E Lakomkin, MA Zamani, C Weber, S Magg, S Wermter RSJ International Conference on Intelligent Robots and Systems (IROS), 854-860, 0 | 4 | |