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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1016/j.jneumeth.2007.09.030openalex-snapshot
Automated video-based facial expression analysis of neuropsychiatric disorders
inferredcites →https://doi.org/10.1016/j.jneumeth.2011.06.023openalex-snapshot
Automated Facial Action Coding System for dynamic analysis of facial expressions in neuropsychiatric disorders
inferredcites →https://doi.org/10.1097/01.nmd.0000138226.22761.39openalex-snapshot
A Review of Studies of the Hamilton Depression Rating Scale in Healthy Controls
inferredcites →https://doi.org/10.1109/acii.2009.5349350openalex-snapshot
OpenEAR — Introducing the munich open-source emotion and affect recognition toolkit
inferredcites →https://doi.org/10.1109/acii.2009.5349358openalex-snapshot
Detecting depression from facial actions and vocal prosody
inferredcites →https://doi.org/10.1109/acii.2013.65openalex-snapshot
Local Gabor Binary Patterns from Three Orthogonal Planes for Automatic Facial Expression Recognition
inferredcites →https://doi.org/10.1109/cvpr.2008.4587630openalex-snapshot
Classification using intersection kernel support vector machines is efficient
inferredcites →https://doi.org/10.1109/fg.2013.6553748openalex-snapshot
Social risk and depression: Evidence from manual and automatic facial expression analysis
inferredcites →https://doi.org/10.1109/fg.2013.6553789openalex-snapshot
Automatic behavior descriptors for psychological disorder analysis
inferredcites →https://doi.org/10.1109/icassp.2013.6637694openalex-snapshot
Real-life voice activity detection with LSTM Recurrent Neural Networks and an application to Hollywood movies
inferredcites →https://doi.org/10.1109/t-affc.2011.27openalex-snapshot
Bridging the Gap between Social Animal and Unsocial Machine: A Survey of Social Signal Processing
inferredcites →https://doi.org/10.1109/t-affc.2012.38openalex-snapshot
Detecting Depression Severity from Vocal Prosody
inferredcites →https://doi.org/10.1109/tpami.2014.16openalex-snapshot
Dynamic Probabilistic CCA for Analysis of Affective Behavior and Fusion of Continuous Annotations
inferredcites →https://doi.org/10.1111/j.1467-9280.2007.02024.xopenalex-snapshot
The World of Emotions is not Two-Dimensional
inferredcites →https://doi.org/10.1111/j.2044-8260.1967.tb00530.xopenalex-snapshot
Development of a Rating Scale for Primary Depressive Illness
inferredcites →https://doi.org/10.1145/1656274.1656278openalex-snapshot
The WEKA data mining software
inferredcites →https://doi.org/10.1145/2512530.2512531openalex-snapshot
Vocal biomarkers of depression based on motor incoordination
inferredcites →https://doi.org/10.1145/2512530.2512532openalex-snapshot
Depression recognition based on dynamic facial and vocal expression features using partial least square regression
inferredcites →https://doi.org/10.1145/2522848.2531742openalex-snapshot
Distribution-based iterative pairwise classification of emotions in the wild using LGBP-TOP
inferredcites →https://doi.org/10.1145/2666253.2666260openalex-snapshot
Automatic Behaviour Understanding in Medicine
inferredcites →https://doi.org/10.1176/appi.ajp.161.12.2163openalex-snapshot
The Hamilton Depression Rating Scale: Has the Gold Standard Become a Lead Weight?
inferredcites →https://doi.org/10.1207/s15327752jpa6703_13openalex-snapshot
Comparison of Beck Depression Inventories-IA and-II in Psychiatric Outpatients
inferredcites →https://doi.org/10.21437/interspeech.2009-103openalex-snapshot
The INTERSPEECH 2009 emotion challenge
inferredcites →https://doi.org/10.21437/interspeech.2010-739openalex-snapshot
The INTERSPEECH 2010 paralinguistic challenge
inferredcites →https://doi.org/10.5220/0004828606710678openalex-snapshot
Fusion of Audio-visual Features using Hierarchical Classifier Systems for the Recognition of Affective States and the State of Depression
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Multivariate Output-Associative Rvm For Multi-Dimensional Affect Predictions
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Intelligent system for depression scale estimation with facial expressions and case study in industrial intelligence
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DepNet: An automated industrial intelligent system using deep learning for video‐based depression analysis
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Fast and Accurate Affect Prediction Using a Hierarchy of Random Forests
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On Annotation and Evaluation of Multi-modal Corpora in Affective Human-Computer Interaction
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Video Summarization for Expression Analysis of Motor Vehicle Operators
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Cascade of Ordinal Classification and Local Regression for Audio-Based Affect Estimation
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Speech and Music Emotion Recognition Using Gaussian Processes
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Audio Visual Recognition of Spontaneous Emotions In-the-Wild
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Automatic Depression Level Analysis Using Audiovisual Modality
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Beyond Accuracy: A Review of Model and Data Trustworthiness in Audio-Visual Depression Recognition
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Research on the Application of Deep Neural Network in the Classification of EEG Data of Major Depressive Disorder and Bipolar Disorder and Optimization Strategy
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Automated facial video-based recognition of depression and anxiety symptom severity: cross-corpus validation
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Deep Affect Prediction in-the-Wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyond
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SETD-Net: Multi-scale spatiotemporal enhancement and multi-granularity temporal difference network for depression detection
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NSANet: Non-Stationary Multi-Scale Attention Network with structured multi-task supervision for video depression detection
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Seeing through the mind: Explainable multimodal AI for depression detection—A review
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PRA-Net: Part-and-Relation Attention Network for depression recognition from facial expression
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TSFFM: Depression detection based on latent association of facial and body expressions
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Identifying robust and dataset-independent acoustic biomarkers of depression through multi-model feature consensus analysis
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Attention-Guided Bi-direction Temporal-aware Network for speech-based depression recognition
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Audio based depression detection using Convolutional Autoencoder
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Depressioner: Facial dynamic representation for automatic depression level prediction
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Video-based emotion recognition in the wild using deep transfer learning and score fusion
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AFEW-VA database for valence and arousal estimation in-the-wild
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Deep learning for depression recognition with audiovisual cues: A review
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Transformer-based multimodal feature enhancement networks for multimodal depression detection integrating video, audio and remote photoplethysmograph signals
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Screening major depressive disorder using vocal acoustic features in the elderly by sex
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Automated depression analysis using convolutional neural networks from speech
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Multimodal big data affective analytics: A comprehensive survey using text, audio, visual and physiological signals
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PointTransform Networks for automatic depression level prediction via facial keypoints
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Personalized calibration for depression detection using knowledge-distilled facial video models
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Efficient and effective strategies for cross-corpus acoustic emotion recognition
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An efficient model-level fusion approach for continuous affect recognition from audiovisual signals
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Automatic depression recognition using CNN with attention mechanism from videos
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A time-frequency channel attention and vectorization network for automatic depression level prediction
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Deep learning-based depression recognition through facial expression: A systematic review
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MPDRM: A Multi-Scale Personalized Depression Recognition Model via facial movements
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Depression level assessment based on multi-layer graph neural networks applied to facial videos
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Attention guided learnable time-domain filterbanks for speech depression detection
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Sequential fusion of facial appearance and dynamics for depression recognition
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A review of depression and suicide risk assessment using speech analysis
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Investigation of different speech types and emotions for detecting depression using different classifiers
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Hierarchical sparse coding framework for speech emotion recognition
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Speech analysis for health: Current state-of-the-art and the increasing impact of deep learning
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Survey on audiovisual emotion recognition: databases, features, and data fusion strategies
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Deep learning in mental health outcome research: a scoping review
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A Multimodal Depression Consultation Dataset of Speech and Text with HAMD-17 Assessments
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Deep spectrotemporal network based depression severity estimation from speech
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Enhancing depression recognition through a mixed expert model by integrating speaker-related and emotion-related features
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Natural language processing applied to mental illness detection: a narrative review
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AI-assisted multi-modal information for the screening of depression: a systematic review and meta-analysis
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A systematic review on automated clinical depression diagnosis
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Automatic depression recognition by intelligent speech signal processing: A systematic survey
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Depression recognition from facial videos: Preprocessing and scheduling choices hide the architectural contributions
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Influence of speaker de‐identification in depression detection
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Assessment method of depressive disorder level based on graph attention network
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LOGLformer: Integrating local and global characteristics for depression scale estimation from facial expressions
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Feasibility study on using house-tree-person drawings for automatic analysis of depression
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Multimodal Biomarkers to Discriminate Cognitive State
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Channel-Dcoupled and Local-Attention Network for Depression Detection
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DepAVF: An Advanced Multimodal Depression Detection Framework Using Audio-Visual Fusion and Adaptive Score Integration
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Multimodal Mild Depression Recognition Based on EEG-EM Synchronization Acquisition Network
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Low-Rank Constrained Latent Domain Adaptation Co-Regression for Robust Depression Recognition
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Discovering Domain-Invariant Subspace for Depression Recognition by Jointly Exploiting Appearance and Dynamics Feature Representations
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Feature Augmenting Networks for Improving Depression Severity Estimation From Speech Signals
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An Investigation on the Audio-Video Data Based Estimation of Emotion Regulation Difficulties and Their Association With Mental Disorders
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Machine Learning in ADHD and Depression Mental Health Diagnosis: A Survey
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A Hybrid Learning-Architecture for Mental Disorder Detection Using Emotion Recognition
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Multi-Modal Fused-Attention Network for Depression Level Recognition Based on Enhanced Audiovisual Cues
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Hierarchical Multi-Task Learning Based on Interactive Multi-Head Attention Feature Fusion for Speech Depression Recognition
inferred← citeshttps://doi.org/10.1109/access.2026.3654434openalex-snapshot
Multimodal Analysis for Depression Recognition Using Stacked Multilevel Deep Neural Networks
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SPLGMMamba: A Self-Paced Learning-Guided Multimodal Mamba Framework for Depression Recognition
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Multimodal dimensional affect recognition using deep bidirectional long short-term memory recurrent neural networks
inferred← citeshttps://doi.org/10.1109/acii.2015.7344580openalex-snapshot
A temporally piece-wise fisher vector approach for depression analysis
inferred← citeshttps://doi.org/10.1109/acii.2015.7344581openalex-snapshot
Multimodal depression recognition with dynamic visual and audio cues
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Multi task sequence learning for depression scale prediction from video
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Objective assessment of depressive symptoms with machine learning and wearable sensors data
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Automatic prediction of Depression and Anxiety from behaviour and personality attributes
inferred← citeshttps://doi.org/10.1109/acii55700.2022.9953894openalex-snapshot
Automatic Detection of Subjective, Annotated and Physiological Stress Responses from Video Data
inferred← citeshttps://doi.org/10.1109/acii59096.2023.10388143openalex-snapshot
Expresso-AI: An Explainable Video-Based Deep Learning Models for Depression Diagnosis
inferred← citeshttps://doi.org/10.1109/aciiasia.2018.8470362openalex-snapshot
The Turkish Audio-Visual Bipolar Disorder Corpus
inferred← citeshttps://doi.org/10.1109/aciiw.2017.8272612openalex-snapshot
Detection of universal cross-cultural depression indicators from the physiological signals of observers
inferred← citeshttps://doi.org/10.1109/aciiw.2019.8925023openalex-snapshot
Audio Visual Multimodal Classification of Bipolar Disorder Episodes
inferred← citeshttps://doi.org/10.1109/aciiw.2019.8925042openalex-snapshot
Speech, Head, and Eye-based Cues for Continuous Affect Prediction
inferred← citeshttps://doi.org/10.1109/aciiw.2019.8925158openalex-snapshot
Local Second-Order Gradient Cross Pattern for Automatic Depression Detection
inferred← citeshttps://doi.org/10.1109/aciiw52867.2021.9666292openalex-snapshot
A simple baseline for evaluating Expression Transfer and Anonymisation in Video Transfer
inferred← citeshttps://doi.org/10.1109/aicsip65423.2025.11427217openalex-snapshot
Graph Neural Network Methods for Depression Recognition in Facial Videos
inferred← citeshttps://doi.org/10.1109/apsipa.2014.7041742openalex-snapshot
Proposal of a depression detector
inferred← citeshttps://doi.org/10.1109/apsipa.2016.7820752openalex-snapshot
Speech analysis and depression
inferred← citeshttps://doi.org/10.1109/asru.2015.7404867openalex-snapshot
A study of social-affective communication: Automatic prediction of emotion triggers and responses in television talk shows
inferred← citeshttps://doi.org/10.1109/bibm.2017.8217966openalex-snapshot
Sentiment analysis and affective computing for depression monitoring
inferred← citeshttps://doi.org/10.1109/bibm.2017.8217968openalex-snapshot
Continuous affect prediction using eye gaze and speech
inferred← citeshttps://doi.org/10.1109/bibm49941.2020.9313597openalex-snapshot
Depression severity prediction from facial expression based on the DRR_DepressionNet network
inferred← citeshttps://doi.org/10.1109/bibm58861.2023.10385416openalex-snapshot
Fusing Local-Global Facial Features by NFFT for Automatic Depression Estimation
inferred← citeshttps://doi.org/10.1109/bibm58861.2023.10385423openalex-snapshot
A Multi-Frame Rate Network with Attention Mechanism for Depression Severity Estimation
inferred← citeshttps://doi.org/10.1109/bibm58861.2023.10385591openalex-snapshot
LI-FPN: Depression and Anxiety Detection from Learning and Imitation
inferred← citeshttps://doi.org/10.1109/bibm62325.2024.10822401openalex-snapshot
Automatic Depression Detection Network Based on Facial Images and Facial Landmarks Feature Fusion
inferred← citeshttps://doi.org/10.1109/bibm62325.2024.10822477openalex-snapshot
Depression Diagnosis Algorithm Based on R2U-Net Using Facial Images
inferred← citeshttps://doi.org/10.1109/bibm66473.2025.11356969openalex-snapshot
Depression Detection from Multilingual Interviews Based on Textual and Audio Feature Fusion
inferred← citeshttps://doi.org/10.1109/cac.2018.8623055openalex-snapshot
Improvement on Speech Depression Recognition Based on Deep Networks
inferred← citeshttps://doi.org/10.1109/caibda70336.2026.11621807openalex-snapshot
CLAP-MDR: Contrastive Language–Audio Pretraining for Multimodal Depression Recognition
inferred← citeshttps://doi.org/10.1109/cfis54774.2022.9756429openalex-snapshot
Determining the Severity of Depression in Speech Based on Combination of Acoustic-Space and Score-Space Features
inferred← citeshttps://doi.org/10.1109/coginfocom.2016.7804533openalex-snapshot
Analyzing the expression of annoyance during phone calls to complaint services
inferred← citeshttps://doi.org/10.1109/coginfocom47531.2019.9089948openalex-snapshot
Can Spontaneous Emotions be Detected from Speech on TV Political Debates?
inferred← citeshttps://doi.org/10.1109/coginfocom50765.2020.9237856openalex-snapshot
Behavioral Sentiment Analysis of Depressive States
inferred← citeshttps://doi.org/10.1109/cvprw.2016.186openalex-snapshot
Facial Affect “In-the-Wild”: A Survey and a New Database
inferred← citeshttps://doi.org/10.1109/cvprw.2017.248openalex-snapshot
Aff-Wild: Valence and Arousal ‘In-the-Wild’ Challenge
inferred← citeshttps://doi.org/10.1109/dicta60407.2023.00048openalex-snapshot
Estimating Depression Severity from Long-Sequence Face Videos via an Ensemble Global Diverse Convolutional Model
inferred← citeshttps://doi.org/10.1109/dsp65409.2025.11074839openalex-snapshot
Wavelet Convolutions for Audio-Visual Fusion in Mental Health Disorder Detection
inferred← citeshttps://doi.org/10.1109/embc.2016.7591564openalex-snapshot
Video-based depression detection using local Curvelet binary patterns in pairwise orthogonal planes
inferred← citeshttps://doi.org/10.1109/embc.2017.8037103openalex-snapshot
Facial geometry and speech analysis for depression detection
inferred← citeshttps://doi.org/10.1109/embc40787.2023.10341118openalex-snapshot
A Novel Technique for Detecting Depressive Disorder: A Speech Database-Based Approach
inferred← citeshttps://doi.org/10.1109/embc53108.2024.10781765openalex-snapshot
Exploring Self-Supervised Models for Depressive Disorder Detection: A Study on Speech Corpora
inferred← citeshttps://doi.org/10.1109/eusipco.2015.7362750openalex-snapshot
Dynamic speech emotion recognition with state-space models
inferred← citeshttps://doi.org/10.1109/fg.2015.7284859openalex-snapshot
Automatic affective dimension recognition from naturalistic facial expressions based on wavelet filtering and PLS regression
inferred← citeshttps://doi.org/10.1109/fg.2015.7284860openalex-snapshot
Automatic 3D facial expression recognition using geometric and textured feature fusion
inferred← citeshttps://doi.org/10.1109/fg.2018.00032openalex-snapshot
Human Behaviour-Based Automatic Depression Analysis Using Hand-Crafted Statistics and Deep Learned Spectral Features
inferred← citeshttps://doi.org/10.1109/fg.2018.00113openalex-snapshot
Deep Learned Cumulative Attribute Regression
inferred← citeshttps://doi.org/10.1109/fg.2018.00116openalex-snapshot
Detecting Depression Severity by Interpretable Representations of Motion Dynamics
inferred← citeshttps://doi.org/10.1109/fg.2019.8756568openalex-snapshot
Combining Global and Local Convolutional 3D Networks for Detecting Depression from Facial Expressions
inferred← citeshttps://doi.org/10.1109/fg57933.2023.10042506openalex-snapshot
Investigating Visual Features for Cognitive Impairment Detection Using In-the-wild Data
inferred← citeshttps://doi.org/10.1109/fg57933.2023.10042796openalex-snapshot
Multimodal Feature Selection for Detecting Mothers' Depression in Dyadic Interactions with their Adolescent Offspring
inferred← citeshttps://doi.org/10.1109/icaci69731.2026.11677792openalex-snapshot
Multi-Scale Non-Stationary Attention Learning for Video-Based Depression Severity Regression
inferred← citeshttps://doi.org/10.1109/icassp.2015.7178876openalex-snapshot
Cross-corpus depression prediction from speech
inferred← citeshttps://doi.org/10.1109/icassp.2015.7178877openalex-snapshot
Effects of feature type, learning algorithm and speaking style for depression detection from speech
inferred← citeshttps://doi.org/10.1109/icassp.2016.7472788openalex-snapshot
Noise and reverberation effects on depression detection from speech
inferred← citeshttps://doi.org/10.1109/icassp.2018.8462011openalex-snapshot
Human-Like Emotion Recognition: Multi-Label Learning from Noisy Labeled Audio-Visual Expressive Speech
inferred← citeshttps://doi.org/10.1109/icassp39728.2021.9413504openalex-snapshot
Multi-Scale and Multi-Region Facial Discriminative Representation for Automatic Depression Level Prediction
inferred← citeshttps://doi.org/10.1109/icassp39728.2021.9414129openalex-snapshot
Estimating Severity of Depression From Acoustic Features and Embeddings of Natural Speech
inferred← citeshttps://doi.org/10.1109/icassp40776.2020.9053207openalex-snapshot
Hierarchical Attention Transfer Networks for Depression Assessment from Speech
inferred← citeshttps://doi.org/10.1109/icassp40776.2020.9054375openalex-snapshot
Encoding Temporal Information For Automatic Depression Recognition From Facial Analysis
inferred← citeshttps://doi.org/10.1109/icassp43922.2022.9746011openalex-snapshot
Csenet: Complex Squeeze-and-Excitation Network for Speech Depression Level Prediction
inferred← citeshttps://doi.org/10.1109/icassp43922.2022.9746569openalex-snapshot
Automatic Depression Detection: an Emotional Audio-Textual Corpus and A Gru/Bilstm-Based Model
inferred← citeshttps://doi.org/10.1109/icassp43922.2022.9747292openalex-snapshot
Automatic Depression Level Assessment from Speech By Long-Term Global Information Embedding
inferred← citeshttps://doi.org/10.1109/icassp48485.2024.10446491openalex-snapshot
MDAVIF: A Multi-Domain Acoustical-Visual Information Fusion Model for Depression Recognition from Vlog Data
inferred← citeshttps://doi.org/10.1109/icassp49660.2025.10888951openalex-snapshot
MFMamba: A Multimodal Fusion State Space Model for Depression Recognition
inferred← citeshttps://doi.org/10.1109/icassp49660.2025.10889188openalex-snapshot
A Frequency-aware Augmentation Network for Mental Disorders Assessment from Audio
inferred← citeshttps://doi.org/10.1109/icassp49660.2025.10889290openalex-snapshot
Efficient Long Speech Sequence Modelling for Time-Domain Depression Level Estimation
inferred← citeshttps://doi.org/10.1109/icassp49660.2025.10889539openalex-snapshot
M 3 ADD: A Novel Benchmark for Physiology Signal-based Automatic Depression Detection with Multimodal Multitask Multievent Framework
inferred← citeshttps://doi.org/10.1109/icassp55912.2026.11462947openalex-snapshot
Tensorformer-Based Multimodal Depression Detection from Concurrent Gait Patterns and Physiological Signals
inferred← citeshttps://doi.org/10.1109/icassp55912.2026.11463256openalex-snapshot
Dual Contrastive Learning for Semi-Supervised Domain Adaptation in Bi-Modal Depression Recognition
inferred← citeshttps://doi.org/10.1109/icassp55912.2026.11463362openalex-snapshot
Efficient Depression Detection from Speech via Language-Independent Prompt-Driven Reprogramming
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