Knowledge Commons of Institute of Automation,CAS
Unsupervised Representation Learning with Future Observation Prediction for Speech Emotion Recognition | |
Zheng Lian1,3; Jianhua Tao1,2,3; Bin Liu1; Jian Huang1,3 | |
2019 | |
会议名称 | Proceedings of the 20st Annual Conference of the International Speech Communication Association (Interspeech 2019) |
会议日期 | 15-19 September, 2019 |
会议地点 | Graz, Austria |
摘要 | Prior works on speech emotion recognition utilize various unsupervised learning approaches to deal with low-resource samples. However, these methods pay less attention to modeling the long-term dynamic dependency, which is important for speech emotion recognition. To deal with this problem, this paper combines the unsupervised representation learning strategy – Future Observation Prediction (FOP), with transfer learning approaches (such as Fine-tuning and Hypercolumns). To verify the effectiveness of the proposed method, we conduct experiments on the IEMOCAP database. Experimental results demonstrate that our method is superior to currently advanced unsupervised learning strategies. |
收录类别 | EI |
语种 | 英语 |
七大方向——子方向分类 | 智能交互 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/44723 |
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, CASIA, 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 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Zheng Lian,Jianhua Tao,Bin Liu,et al. Unsupervised Representation Learning with Future Observation Prediction for Speech Emotion Recognition[C],2019. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Unsupervised Represe(373KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 |
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