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Multi Task Sequence Learning for Depression Scale Prediction from Video
Linlin Chao; Jianhua Tao; Minghao Yang; Ya Li
2015
会议名称International Conference on Affective Computing and Intelligent Interaction
会议录名称International Conference on Affective Computing and Intelligent Interaction
页码368-373
会议日期2015-9
会议地点Xi'an, China
摘要1; Depression is a typical mood disorder, which affects people in mental and even physical problems. People who suffer depression always behave abnormal in visual behavior and the voice. In this paper, an audio visual based multimodal depression scale prediction system is proposed. Firstly, features are extracted from video and audio are fused in feature level to represent the audio visual behavior. Secondly, long short memory recurrent neural network (LSTM-RNN) is utilized to encode the dynamic temporal information of the abnormal audio visual behavior. Thirdly, emotion information is utilized by multi-task learning to boost the performance further. The proposed approach is evaluated on the Audio-Visual Emotion Challenge (AVEC2014) dataset. Experiments results show the dimensional emotion recognition helps to depression scale prediction.
关键词Depression Recognition
收录类别EI
语种英语
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/11849
专题模式识别国家重点实验室_语音交互
通讯作者Linlin Chao
作者单位Institute of Automation, Chinese Academy of Sciences
推荐引用方式
GB/T 7714
Linlin Chao,Jianhua Tao,Minghao Yang,et al. Multi Task Sequence Learning for Depression Scale Prediction from Video[C],2015:368-373.
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