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

• Published in: Frontiers in neurology Vol. 15; p. 1394210
• Main Authors: Ji, Jun, Dong, Wentian, Li, Jiaqi, Peng, Jingzhu, Feng, Chaonan, Liu, Rujia, Shi, Chuan, Ma, Yantao
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 04.07.2024
• Subjects: depression; machine learning; mania; mood state detection; Neurology; voice biomarker; voice biomarker; mood state detection; depression; machine learning; mania
• ISSN: 1664-2295; 1664-2295
• DOI: 10.3389/fneur.2024.1394210

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). Our results indicated that the Chinese-speech-pretrain-GRU model performed the best, achieving sensitivities of 77.5% and 54.8% and specificities of 86.1% and 90.3% for detecting depressive and manic states, respectively, with an overall accuracy of 80.2%. These findings show that machine learning can reliably differentiate between depressive and manic mood states via voice analysis, allowing for a more objective and precise approach to mood disorder assessment.