BackgroundThis paper aims to synthesise the literature on machine learning (ML) and big
data applications for mental health, highlighting current research and applications in …

High prevalence of mental illness and the need for effective mental health care, combined
with recent advances in AI, has led to an increase in explorations of how the field of machine …

This paper is the first review into the automatic analysis of speech for use as an objective
predictor of depression and suicidality. Both conditions are major public health concerns; …

This paper presents a novel and effective audio based method on depression classification.
It focuses on two important issues,\emph {ie} data representation and sample imbalance …

To help clinicians to efficiently diagnose the severity of a person's depression, the affective
computing community and the artificial intelligence field have shown a growing interest in …

A human being's cognitive system can be simulated by artificial intelligent systems.
Machines and robots equipped with cognitive capability can automatically recognize a …

This paper addresses multi-modal depression analysis. We propose a multi-modal fusion
framework composed of deep convolutional neural network (DCNN) and deep neural …

This paper presents our system design for the Audio-Visual Emotion Challenge (AV^+EC
2015). Besides the baseline features, we extract from audio the functionals on low-level …

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 …

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 …