9 research outputs found
The Verbal and Non Verbal Signals of Depression -- Combining Acoustics, Text and Visuals for Estimating Depression Level
Depression is a serious medical condition that is suffered by a large number
of people around the world. It significantly affects the way one feels, causing
a persistent lowering of mood. In this paper, we propose a novel
attention-based deep neural network which facilitates the fusion of various
modalities. We use this network to regress the depression level. Acoustic, text
and visual modalities have been used to train our proposed network. Various
experiments have been carried out on the benchmark dataset, namely, Distress
Analysis Interview Corpus - a Wizard of Oz (DAIC-WOZ). From the results, we
empirically justify that the fusion of all three modalities helps in giving the
most accurate estimation of depression level. Our proposed approach outperforms
the state-of-the-art by 7.17% on root mean squared error (RMSE) and 8.08% on
mean absolute error (MAE).Comment: 10 pages including references, 2 figure
Improving Depression Level Estimation by Concurrently Learning Emotion Intensity
International audienc
Gender-aware Estimation of Depression Severity Level in a Multimodal Setting
International audienc
Some studies on the mechanism of action of luteinizing-hormone
International audienc
Prédiction Automatique des Scores aux Questionnaires PHQ-8 par Intelligence Artificielle
International audienc
Improving Depression Level Estimation by Concurrently Learning Emotion Intensity
International audienc