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Re-examining the robustness of voice features in predicting depression: Compared with baseline of confounders
Pan, Wei1,2; Flint, Jonathan3; Shenhav, Liat4; Liu, Tianli5; Liu, Mingming1,2; Hu, Bin6; Zhu, Tingshao1
First AuthorPan, Wei
Correspondent Emailtszhu@psych.ac.cn
2019-06-20
Source PublicationPLOS ONE
ISSN1932-6203
Subtypearticle
Volume14Issue:6Pages:14
Contribution Rank1
Abstract

A large proportion of Depression Disorder patients do not receive an effective diagnosis, which makes it necessary to find a more objective assessment to facilitate a more rapid and accurate diagnosis of depression. Speech data is easy to acquire clinically, its association with depression has been studied, although the actual predictive effect of voice features has not been examined. Thus, we do not have a general understanding of the extent to which voice features contribute to the identification of depression. In this study, we investigated the significance of the association between voice features and depression using binary logistic regression, and the actual classification effect of voice features on depression was re-examined through classification modeling. Nearly 1000 Chinese females participated in this study. Several different datasets was included as test set. We found that 4 voice features (PC1, PC6, PC17, PC24, P<0.05, corrected) made significant contribution to depression, and that the contribution effect of the voice features alone reached 35.65% (Nagelkerke's R-2). In classification modeling, voice data based model has consistently higher predicting accuracy(F-measure) than the baseline model of demographic data when tested on different datasets, even across different emotion context. F-measure of voice features alone reached 81%, consistent with existing data. These results demonstrate that voice features are effective in predicting depression and indicate that more sophisticated models based on voice features can be built to help in clinical diagnosis.

DOI10.1371/journal.pone.0218172
Indexed BySCI
Language英语
Funding OrganizationNational Basic Research Program of China
Funding ProjectNational Basic Research Program of China[2014CB744600]
WOS Research AreaScience & Technology - Other Topics
WOS SubjectMultidisciplinary Sciences
WOS IDWOS:000484893500028
PublisherPUBLIC LIBRARY SCIENCE
WOS KeywordHAN CHINESE WOMEN ; CLINICAL DEPRESSION ; MENTAL-DISORDERS ; FIELD TRIALS ; SPEECH ; EMOTION ; BURDEN ; CLASSIFICATION ; EPIDEMIOLOGY ; DISABILITY
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.psych.ac.cn/handle/311026/29828
Collection社会与工程心理学研究室
Corresponding AuthorZhu, Tingshao
Affiliation1.Chinese Acad Sci, Inst Psychol, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Beijing, Peoples R China
3.Univ Calif Los Angeles, Semel Inst Neurosci & Human Behav, Ctr Neurobehav Genet, Los Angeles, CA 90024 USA
4.Univ Calif Los Angeles, Dept Comp Sci, Los Angeles, CA 90024 USA
5.Peking Univ, Inst Populat Res, Beijing, Peoples R China
6.Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou, Gansu, Peoples R China
Recommended Citation
GB/T 7714
Pan, Wei,Flint, Jonathan,Shenhav, Liat,et al. Re-examining the robustness of voice features in predicting depression: Compared with baseline of confounders[J]. PLOS ONE,2019,14(6):14.
APA Pan, Wei.,Flint, Jonathan.,Shenhav, Liat.,Liu, Tianli.,Liu, Mingming.,...&Zhu, Tingshao.(2019).Re-examining the robustness of voice features in predicting depression: Compared with baseline of confounders.PLOS ONE,14(6),14.
MLA Pan, Wei,et al."Re-examining the robustness of voice features in predicting depression: Compared with baseline of confounders".PLOS ONE 14.6(2019):14.
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