Funder
Ministry of Science and ICT, South Korea
NRF
Subject
Artificial Intelligence,Computer Science Applications,General Engineering
Reference28 articles.
1. Abdullah S., Ameen S., Sadeeq M., Zeebaree S., Trends T. (2021). Multimodal emotion recognition using deep learning. 2(02). 52-58.
2. Busso, C., Bulut, M., Lee, C.-C., Kazemzadeh, A., Mower, E., Kim, S. (2008). IEMOCAP: Interactive emotional dyadic motion capture database. 42(4), 335-359.
3. Chen, F., Shao, J., Zhu, A., Ouyang, D., Liu, X., & Shen, H. (2022). Modeling hierarchical uncertainty for multimodal emotion recognition in conversation.
4. Chen F., Sun Z., Ouyang D., Liu X., & Shao J. (2021). Learning what and when to drop: Adaptive multimodal and contextual dynamics for emotion recognition in conversation. Paper presented at the Proceedings of the 29th ACM International Conference on Multimedia.
5. Cho, J., Pappagari, R., Kulkarni, P., Villalba, J., Carmiel, Y., & Dehak, N. (2019). Deep neural networks for emotion recognition combining audio and transcripts.
Cited by
14 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献