End-to-end multimodal clinical depression recognition using deep neural networks: A comparative analysis
M Muzammel, H Salam, A Othmani - Computer Methods and Programs in …, 2021 - Elsevier
Abstract Background and Objective: Major Depressive Disorder is a highly prevalent and
disabling mental health condition. Numerous studies explored multimodal fusion systems …
disabling mental health condition. Numerous studies explored multimodal fusion systems …
Automatic detection of depression in speech using ensemble convolutional neural networks
A Vázquez-Romero, A Gallardo-Antolín - Entropy, 2020 - mdpi.com
This paper proposes a speech-based method for automatic depression classification. The
system is based on ensemble learning for Convolutional Neural Networks (CNNs) and is …
system is based on ensemble learning for Convolutional Neural Networks (CNNs) and is …
Decision tree based depression classification from audio video and language information
In order to improve the recognition accuracy of the Depression Classification Sub-Challenge
(DCC) of the AVEC 2016, in this paper we propose a decision tree for depression …
(DCC) of the AVEC 2016, in this paper we propose a decision tree for depression …
Deep learning-based classification of posttraumatic stress disorder and depression following trauma utilizing visual and auditory markers of arousal and mood
BackgroundVisual and auditory signs of patient functioning have long been used for clinical
diagnosis, treatment selection, and prognosis. Direct measurement and quantification of …
diagnosis, treatment selection, and prognosis. Direct measurement and quantification of …
AudVowelConsNet: A phoneme-level based deep CNN architecture for clinical depression diagnosis
Depression is a common and serious mood disorder that negatively affects the patient's
capacity of functioning normally in daily tasks. Speech is proven to be a vigorous tool in …
capacity of functioning normally in daily tasks. Speech is proven to be a vigorous tool in …
A systematic review on automated clinical depression diagnosis
Assessing mental health disorders and determining treatment can be difficult for a number of
reasons, including access to healthcare providers. Assessments and treatments may not be …
reasons, including access to healthcare providers. Assessments and treatments may not be …
Integrating deep and shallow models for multi-modal depression analysis—hybrid architectures
At present, although great progress has been made in automatic depression assessment,
most of the recent works only concern the audio and video paralinguistic information, rather …
most of the recent works only concern the audio and video paralinguistic information, rather …
A random forest regression method with selected-text feature for depression assessment
B Sun, Y Zhang, J He, L Yu, Q Xu, D Li… - Proceedings of the 7th …, 2017 - dl.acm.org
Audio/visual and mood disorder cues have been recently explored to assist psychologists
and psychiatrists in Depression Diagnosis. In this paper, we propose a random forest …
and psychiatrists in Depression Diagnosis. In this paper, we propose a random forest …
Early mental health uncovering with short scripted and unscripted voice recordings
Mental illnesses are often undiagnosed, highlighting the need for an effective alternative to
traditional screening surveys. We propose our Early Mental Health Uncovering (EMU) …
traditional screening surveys. We propose our Early Mental Health Uncovering (EMU) …
Audiface: Multimodal deep learning for depression screening
Depression is a very common mental health disorder with a devastating social and
economic impact. It can be costly and difficult to detect, traditionally requiring a significant …
economic impact. It can be costly and difficult to detect, traditionally requiring a significant …