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Detection of depression in adolescents based on statistical modeling of emotional influences in parent-adolescent conversations.

, , and . ICASSP, page 987-991. IEEE, (2015)

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Study of empirical mode decomposition and spectral analysis for stress and emotion classification in natural speech., , , and . Biomed. Signal Process. Control., 6 (2): 139-146 (2011)Interaction between hypothalamic-pituitary-adrenal axis genetic variation and maternal behavior in the prediction of amygdala connectivity in children., , , , , , , , and . NeuroImage, (2019)Influence of acoustic low-level descriptors in the detection of clinical depression in adolescents., , , , and . ICASSP, page 5154-5157. IEEE, (2010)Neural Mixed Effects for Nonlinear Personalized Predictions., , , , , and . ICMI, page 445-454. ACM, (2023)Research Protocol for the Google Health Digital Well-being Study., , , , , , , , and . CoRR, (2023)Language Use in Mother-Adolescent Dyadic Interaction: Preliminary Results., , , , , , and . ACII, page 1-8. IEEE, (2022)Guidelines for wrist-worn consumer wearable assessment of heart rate in biobehavioral research., , , , , and . npj Digit. Medicine, (2020)Recognition of stress in speech using wavelet analysis and Teager energy operator., , , and . INTERSPEECH, page 605-608. ISCA, (2008)Statistical differences in speech acoustics of major depressed and non-depressed adolescents., , and . ICSPCS, page 1-7. IEEE, (2015)Prefrontal Structural Correlates of Cognitive Control during Adolescent Development: A 4-Year Longitudinal Study., , , , , and . J. Cognitive Neuroscience, 26 (5): 1118-1130 (2014)