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

Wei Pan, Jonathan Flint, Liat Shenhav, Tianli Liu, Mingming Liu, Bin Hu and Tingshao Zhu

PLOS ONE, 2019, vol. 14, issue 6, 1-14

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

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0218172

DOI: 10.1371/journal.pone.0218172

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