Depressive and mania mood state detection through voice as a biomarker using machine learning.

ABSTRACT:

Introduction

Depressive and manic states contribute significantly to the global social burden, but objective detection tools are still lacking. This study investigates the feasibility of utilizing voice as a biomarker to detect these mood states. Methods:From real-world emotional journal voice recordings, 22 features were retrieved in this study, 21 of which showed significant differences among mood states. Additionally, we applied leave-one-subject-out strategy to train and validate four classification models: Chinese-speech-pretrain-GRU, Gate Recurrent Unit (GRU), Bi-directional Long Short-Term Memory (BiLSTM), and Linear Discriminant Analysis (LDA).

Results

Our results indicated that the Chinese-speech-pretrain-GRU model performed the best, achieving sensitivities of 77.5

SUBMITTER: Ji J 

PROVIDER: S-EPMC11254794 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature