Re-examining the robustness of voice features in predicting depression: Compared with baseline of confounders

doi:10.1371/journal.pone.0218172

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	Re-examining the robustness of voice features in predicting depression: Compared with baseline of confounders
	Pan, Wei 1,2; Flint, Jonathan 3; Shenhav, Liat 4; Liu, Tianli 5; Liu, Mingming 1,2; Hu, Bin 6; Zhu, Tingshao 1
通讯作者	Zhu, Tingshao(tszhu@psych.ac.cn)
摘要	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.
	2019-06-20
语种	英语
DOI	10.1371/journal.pone.0218172
发表期刊	PLOS ONE
ISSN	1932-6203
卷号	14 期号:6 页码:14
收录类别	SCI ; SCI
资助项目	National Basic Research Program of China[2014CB744600]
出版者	PUBLIC LIBRARY SCIENCE
WOS关键词	HAN CHINESE WOMEN ; CLINICAL DEPRESSION ; MENTAL-DISORDERS ; FIELD TRIALS ; SPEECH ; EMOTION ; BURDEN ; CLASSIFICATION ; EPIDEMIOLOGY ; DISABILITY
WOS研究方向	Science & Technology - Other Topics
WOS类目	Multidisciplinary Sciences
WOS记录号	WOS:000484893500028
资助机构	National Basic Research Program of China
引用统计
文献类型	期刊论文
条目标识符	http://ir.psych.ac.cn/handle/311026/29826
通讯作者	Zhu, Tingshao
作者单位	1.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
推荐引用方式 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.