Knowledge Commons of Institute of Automation,CAS
Speech emotion recognition using semi-supervised learning with ladder networks | |
Huang, Jian1,3![]() ![]() ![]() ![]() ![]() | |
2018-05 | |
会议名称 | Processeeding of IEEE 2018 First Asian Conference on Affective Computing and Intelligent Interaction (ACII Asia 2018) |
会议日期 | 2018.5.20-2018.5.22 |
会议地点 | Beijing, China |
摘要 | As a major branch of speech processing, speech emotion recognition has drawn much attention of researchers. Prior works have proposed a variety of models and feature sets for training a system. In this paper, we propose to use semi-supervised learning with ladder networks to generate robust feature representation for speech emotion recognition. In our method, the input of ladder network is the normalized static acoustic features and is mapped to high level hidden representations. The model is trained to simultaneously minimize the sum of supervised and unsupervised cost functions by back propagation. The extracted hidden representations are used as emotional features in SVM model for speech emotion recognition. The experimental results, performed on IEMOCAP database, show 2.6% higher performance than denoising auto-encoder, and 5.3% than the static acoustic features. |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/39307 |
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. Speech emotion recognition using semi-supervised learning with ladder networks[C],2018. |
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AACII.pdf(396KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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