Knowledge Commons of Institute of Automation,CAS
Efficient Modeling of Long Temporal Contexts for Continuous Emotion Recognition | |
Huang, Jian1,3![]() ![]() ![]() ![]() ![]() | |
2019-09 | |
会议名称 | Processeeding of 8th International Conference on Affective Computing and Intelligent Interaction (ACII 2019) |
会议日期 | 2019.9.3-2019.9.6 |
会议地点 | Cambridge, United Kingdom |
摘要 | Continuous emotion recognition is a challenging task due to its difficulty in modeling long-term contexts dependencies. Prior researches have exploited emotional temporal contexts from two perspectives, which are based on feature representations and emotional models. In this paper, we explore the model based approaches for continuous emotion recognition. Specifically, three temporal models including LSTM, TDNN and multi-head attention models are utilized to learn long-term contexts dependencies based on short-term feature representations. The temporal information learned by the temporal models allows the network to more easily exploit the slow changing dynamics between emotional states. Our experimental results demonstrate that the temporal models can model emotional long-term dynamic information effectively. Multi-head attention model achieves best performance among three models and multi-model combination models further improve the performance of continuous emotion recognition significantly. |
语种 | 英语 |
七大方向——子方向分类 | 多模态智能 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/39305 |
专题 | 多模态人工智能系统全国重点实验室_智能交互 |
作者单位 | 1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China 2.CAS Center for Excellence in Brain Science and Intelligence Technology, Beijing, China 3.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China |
第一作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Huang, Jian,Tao, Jianhua,Liu, Bin,et al. Efficient Modeling of Long Temporal Contexts for Continuous Emotion Recognition[C],2019. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
ACII_86.pdf(420KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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