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Detecting postpartum depression in depressed people by speech features
Wang, Jingying1,2; Sui, Xiaoyun1; Hu, Bin3; Flint, Jonathan4; Bai, Shuotian5; Gao, Yuanbo2; Zhou, Yang1,2; Zhu, Tingshao1
First AuthorWang, Jingying
2018
Conference Name3rd International Conference on Human Centered Computing, HCC 2017
Correspondent Emailtszhu@psych.ac.cn
Source PublicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10745 LNCS
Pages433-442
Conference DateAugust 7, 2017 - August 9, 2017
Conference PlaceKazan
CountryRussia
PublisherSpringer Verlag
Contribution Rank1
Abstract

Postpartum depression (PPD) is a depressive disorder with peripartum onset, which brings heavy burden to individuals and their families. In this paper, we propose to detect PPD in depressed people via voices. We used openSMILE for feature extraction, selected Sequential Floating Forward Selection (SFFS) algorithm for feature selection, tried different settings of features, set 5-fold cross validation and applied Support Vector Machine (SVM) on Weka for training and testing different models. The best predictive performance among our models is 69%, which suggests that the speech features could be used as a potential behavioral indicator for identifying PPD in depression. We also found that a combined impact of features and content of questions contribute to the prediction. After dimension reduction, the average value of F-measure was increased 5.2%, and the precision of PPD was rose to 75%. Comparing with demographic questions, the features of emotional induction questions have better predictive effects. © Springer International Publishing AG 2018.

Subject AreaFeature Extraction
DOI10.1007/978-3-319-74521-3_46
ISBN03029743
Indexed ByEI
Language英语
EI KeywordsClassification (Of Information) - Speech Recognition - Support Vector Machines
Citation statistics
Document Type会议论文
Identifierhttp://ir.psych.ac.cn/handle/311026/27752
Collection中国科学院行为科学重点实验室
Affiliation1.Institute of Psychology, Chinese Academy of Sciences, Beijing; 100101, China;
2.University of Chinese Academy of Sciences, Beijing; 100049, China;
3.School of Information Science and Engineering, Lanzhou University, Gansu; 730000, China;
4.Department of Psychiatry and Biobehavioral Sciences, UCLA David Geffen School of Medicine, Los Angeles; CA; 90095, United States;
5.School of Information Engineering, Hubei University of Economics, Wuhan; 430205, China
First Author AffilicationInstitute of Psychology, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Wang, Jingying,Sui, Xiaoyun,Hu, Bin,et al. Detecting postpartum depression in depressed people by speech features[C]:Springer Verlag,2018:433-442.
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