Name
Abstract | ||
|---|---|---|
Depression is a major debilitating disorder which can affect people from all ages. With a continuous increase in the number of annual cases of depression, there is a need to develop automatic techniques for the detection of the presence and its severity. We explore different modalities (speech, behavioral characteristics, language and visual features extracted from face) to design and develop automatic methods for the detection of depression. In psychology literature, the eight-item Patient Health Questionnaire depression scale (PHQ-8) is well established as a tool for measuring the severity of depression. In this paper we aim to automatically predict the total sum of PHQ-8 scores from features extracted from the different modalities. We demonstrate that among the considered modalities, behavioral characteristic features extracted from speech yield the lowest MAE, outperforming the best system at the Audio/Visual Emotion Challenge (AVEC) 2017 depression sub-challenge. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/HealthCom.2018.8531119 | 2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom) |
Keywords | DocType | Volume |
Affective Computing,Depression Detection,Machine Learning,Speech,Natural Language Processing,Facial Expressions | Conference | abs/1711.06095 |
ISBN | Citations | PageRank |
978-1-5386-4295-5 | 1 | 0.35 |
References | Authors | |
37 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Evgeny A. Stepanov | 1 | 51 | 11.57 |
| Stéphane Lathuilière | 2 | 33 | 5.98 |
| Shammur Absar Chowdhury | 3 | 28 | 6.01 |
| Arindam Ghosh | 4 | 86 | 21.65 |
| Radu-Laurentiu Vieriu | 5 | 30 | 2.10 |
| Nicu Sebe | 6 | 7013 | 403.03 |
| Giuseppe Riccardi | 7 | 1046 | 101.15 |