Depression Detection
26 papers with code • 4 benchmarks • 5 datasets
Depression Detection is the problem of identifying signs of depression in individuals. These signs might be identified in peoples’ speech, facial expressions and in the use of language.
Benchmarks
These leaderboards are used to track progress in Depression Detection
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
Datasets
SMHD
Perla Dataset
Latest papers with no code
Few-Shot Learning for Chronic Disease Management: Leveraging Large Language Models and Multi-Prompt Engineering with Medical Knowledge Injection
On the depression detection task, our method (F1 = 0. 975~0. 978) significantly outperforms traditional supervised learning paradigms, including feature engineering (F1 = 0. 760) and architecture engineering (F1 = 0. 756).
Harnessing Large Language Models Over Transformer Models for Detecting Bengali Depressive Social Media Text: A Comprehensive Study
The study categorized Reddit and X datasets into "Depressive" and "Non-Depressive" segments, translated into Bengali by native speakers with expertise in mental health, resulting in the creation of the Bengali Social Media Depressive Dataset (BSMDD).
CANAMRF: An Attention-Based Model for Multimodal Depression Detection
Multimodal depression detection is an important research topic that aims to predict human mental states using multimodal data.
Early Detection of Depression and Eating Disorders in Spanish: UNSL at MentalRiskES 2023
To solve these tasks, we proposed models based on Transformers followed by a decision policy according to criteria defined by an early detection framework.
Using Audio Data to Facilitate Depression Risk Assessment in Primary Health Care
The objectives were to: 1) Collect audio data from 24 people (12 with depression and 12 without mental health or major health condition diagnoses); 2) Build a machine learning model to predict depression risk.
Transferring speech-generic and depression-specific knowledge for Alzheimer's disease detection
Apart from the knowledge from speech-generic representations, this paper also proposes to simultaneously transfer the knowledge from a speech depression detection task based on the high comorbidity rates of depression and AD.
Hierarchical attention interpretation: an interpretable speech-level transformer for bi-modal depression detection
These interpretations allow clinicians to verify the validity of predictions made by depression detection tools, promoting their clinical implementations.
DepreSym: A Depression Symptom Annotated Corpus and the Role of LLMs as Assessors of Psychological Markers
The eRisk initiative fosters research on this area and has recently proposed a new ranking task focused on developing search methods to find sentences related to depressive symptoms.
Explainable Depression Detection via Head Motion Patterns
While depression has been studied via multimodal non-verbal behavioural cues, head motion behaviour has not received much attention as a biomarker.
The Relationship Between Speech Features Changes When You Get Depressed: Feature Correlations for Improving Speed and Performance of Depression Detection
The results show that the models used in the experiments improve in terms of training speed and performance when fed with feature correlation matrices rather than with feature vectors.
