Knowledge Commons of Institute of Automation,CAS
Multimodal continuous emotion recognition with data augmentation using recurrent neural networks | |
Huang, Jian1,3![]() ![]() ![]() ![]() ![]() ![]() | |
2018-10 | |
会议名称 | Proceedings of the 2018 on Audio/Visual Emotion Challenge and Workshop, ACM 2018 |
会议日期 | 2018.10.22-2018.10.26 |
会议地点 | Seoul, Republic of Korea |
摘要 | This paper presents our effects for Cross-cultural Emotion Subchallenge in the Audio/Visual Emotion Challenge (AVEC) 2018, whose goal is to predict the level of three emotional dimensions time-continuously in a cross-cultural setup. We extract the emotional features from audio, visual and textual modalities. The state of art regressor for continuous emotion recognition, long short term memory recurrent neural network (LSTM-RNN) is utilized. We augment the training data by replacing the original training samples with shorter overlapping samples extracted from them, thus multiplying the number of training samples and also beneficial to train emotional temporal model with LSTM-RNN. In addition, two strategies are explored to decrease the interlocutor influence to improve the performance. We also compare the performance of feature level fusion and decision level fusion. The experimental results show the efficiency of the proposed method and competitive results are obtained. |
语种 | 英语 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/39302 |
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,Li, Ya,Tao, Jianhua,et al. Multimodal continuous emotion recognition with data augmentation using recurrent neural networks[C],2018. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
AVEC2018-CES-huang.p(8467KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论