CASIA OpenIR  > 模式识别国家重点实验室  > 语音交互
Multi Task Sequence Learning for Depression Scale Prediction from Video
Linlin Chao; Jianhua Tao; Minghao Yang; Ya Li
2015
Conference NameInternational Conference on Affective Computing and Intelligent Interaction
Source PublicationInternational Conference on Affective Computing and Intelligent Interaction
Pages368-373
Conference Date2015-9
Conference PlaceXi'an, China
Abstract1; 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.
KeywordDepression Recognition
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/11849
Collection模式识别国家重点实验室_语音交互
Corresponding AuthorLinlin Chao
AffiliationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
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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