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Human Behaviour-Based Automatic Depression Analysis Using Hand-Crafted Statistics and Deep Learned Spectral Features
16 references·16 citing·39 second hopsampled
Human Behaviour-Based Automatic Depression Analysis Using Hand-Crafted Statistics and Deep Learned Spectral Features (2018)Human Behaviour-Based Automatic Depressio…Dynamic Multimodal Measurement of Depression Severity Using Deep Autoencoding (2017)Dynamic Multimodal Measuremen…Automated Depression Diagnosis Based on Deep Networks to Encode Facial Appearance and Dynamics (2017)Automated Depression Diagnosi…Multimodal and Multiresolution Depression Detection from Speech and Facial Landmark Features (2016)Multimodal and Multiresolutio…Detecting Depression using Vocal, Facial and Semantic Communication Cues (2016)Detecting Depression using Vo…DepAudioNet (2016)DepAudioNetAVEC 2016 (2016)AVEC 2016Automated Depression Diagnosis Based on Facial Dynamic Analysis and Sparse Coding (2015)Automated Depression Diagnosi…Vocal and Facial Biomarkers of Depression based on Motor Incoordination and Timing (2014)Vocal and Facial Biomarkers o…The Distress Analysis Interview Corpus of human and computer interviews (2014)The Distress Analysis Intervi…Psychiatric diagnosis: impersonal, imperfect and important (2014)Psychiatric diagnosis: impers…AVEC 2014 (2014)AVEC 2014Vocal biomarkers of depression based on motor incoordination (2013)Vocal biomarkers of depressio…Social risk and depression: Evidence from manual and automatic facial expression analysis (2013)Social risk and depression: E…Nonverbal social withdrawal in depression: Evidence from manual and automatic analyses (2013)Nonverbal social withdrawal i…AVEC 2013 (2013)AVEC 2013Detecting depression from facial actions and vocal prosody (2009)Detecting depression from fac…Multimodal Sensing for Depression Risk Detection: Integrating Audio, Video, and Text Data (2024)Multimodal Sensing for Depres…Harnessing multimodal approaches for depression detection using large language models and facial expressions (2024)Harnessing multimodal approac…Unlocking the Emotional World of Visual Media: An Overview of the Science, Research, and Impact of Understanding Emotion (2023)Unlocking the Emotional World…Semi-Structural Interview-Based Chinese Multimodal Depression Corpus Towards Automatic Preliminary Screening of Depressive Disorders (2022)Semi-Structural Interview-Bas…E-Prevention: Advanced Support System for Monitoring and Relapse Prevention in Patients with Psychotic Disorders Analyzing Long-Term Multimodal Data from Wearables and Video Captures (2022)E-Prevention: Advanced Suppor…End-to-end multimodal clinical depression recognition using deep neural networks: A comparative analysis (2021)End-to-end multimodal clinica…Interpretation of Depression Detection Models via Feature Selection Methods (2020)Interpretation of Depression …Hybrid CNN-SVM classifier for efficient depression detection system (2020)Hybrid CNN-SVM classifier for…Facial Depression Recognition by Deep Joint Label Distribution and Metric Learning (2020)Facial Depression Recognition…Encoding Temporal Information For Automatic Depression Recognition From Facial Analysis (2020)Encoding Temporal Information…Depression Level Prediction Using Deep Spatiotemporal Features and Multilayer Bi-LTSM (2020)Depression Level Prediction U…AudVowelConsNet: A phoneme-level based deep CNN architecture for clinical depression diagnosis (2020)AudVowelConsNet: A phoneme-le…A Deep Multiscale Spatiotemporal Network for Assessing Depression From Facial Dynamics (2020)A Deep Multiscale Spatiotempo…Combining Global and Local Convolutional 3D Networks for Detecting Depression from Facial Expressions (2019)Combining Global and Local Co…A Multi-Modal Hierarchical Recurrent Neural Network for Depression Detection (2019)A Multi-Modal Hierarchical Re…Integrating Deep and Shallow Models for Multi-Modal Depression Analysis—Hybrid Architectures (2018)Integrating Deep and Shallow …Development of a Rating Scale for Primary Depressive Illness (1967)Comparison of Beck Depression Inventories-IA and-II in Psychiatric Outpatients (1996)Affective Computing (1997)EEG Based Emotion Recognition: A Tutorial and Review (2022)Facial signs of emotional experience. (1980)The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression (2010)The computer expression recognition toolbox (CERT) (2011)Multimodal Depression Detection: Fusion Analysis of Paralinguistic, Head Pose and Eye Gaze Behaviors (2016)Multimodal Measurement of Depression Using Deep Learning Models (2017)A Coefficient of Agreement for Nominal Scales (1960)Intraclass correlations: Uses in assessing rater reliability. (1979)Diagnostic and Statistical Manual of Mental Disorders (2013)Automatic Assessment of Depression Based on Visual Cues: A Systematic Review (2017)Deep learning for depression recognition with audiovisual cues: A review (2021)A RATING SCALE FOR DEPRESSION (1960)Diagnostic and Statistical Manual of Mental Disorders (2008)SimSensei kiosk: a virtual human interviewer for healthcare decision support (2014)AVEC 2019 Workshop and Challenge: State-of-Mind, Detecting Depression with AI, and Cross-Cultural Affect Recognition (2019)Manual for the State-Trait Anxiety Inventory (1970)The PHQ-9: A New Depression Diagnostic and Severity Measure (2002)Individual differences in two emotion regulation processes: Implications for affect, relationships, and well-being. (2003)Automated assessment of psychiatric disorders using speech: A systematic review (2020)Coefficient Alpha and the Internal Structure of Tests (1951)A Concordance Correlation Coefficient to Evaluate Reproducibility (1989)AVEC 2017 (2017)Vocal affect expression: A review and a model for future research. (1986)The PHQ-8 as a measure of current depression in the general population (2008)A review of depression and suicide risk assessment using speech analysis (2015)Visually Interpretable Representation Learning for Depression Recognition from Facial Images (2018)MFCC-based Recurrent Neural Network for automatic clinical depression recognition and assessment from speech (2021)Durational variability in speech and the Rhythm Class Hypothesis (2002)Predicting Depression via Social Media (2021)A multimodal fusion model with multi-level attention mechanism for depression detection (2022)Automatic Depression Detection: an Emotional Audio-Textual Corpus and A Gru/Bilstm-Based Model (2022)The Epidemiology of Major Depressive Disorder (2003)Automated depression analysis using convolutional neural networks from speech (2018)Aff-Wild: Valence and Arousal ‘In-the-Wild’ Challenge (2017)Deep Affect Prediction in-the-Wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyond (2019)Natural language processing applied to mental illness detection: a narrative review (2022)
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References

  • 2014The Distress Analysis Interview Corpus of human and computer interviews591 cited
  • 2013AVEC 2013584 cited
  • 2016AVEC 2016505 cited
  • 2009Detecting depression from facial actions and vocal prosody494 cited
  • 2014AVEC 2014475 cited
  • 2016DepAudioNet299 cited
  • 2017Automated Depression Diagnosis Based on Deep Networks to Encode Facial Appearance and Dynamics260 cited
  • 2013Nonverbal social withdrawal in depression: Evidence from manual and automatic analyses255 cited
  • 2016Detecting Depression using Vocal, Facial and Semantic Communication Cues218 cited
  • 2014Vocal and Facial Biomarkers of Depression based on Motor Incoordination and Timing209 cited
  • 2013Social risk and depression: Evidence from manual and automatic facial expression analysis199 cited
  • 2017Dynamic Multimodal Measurement of Depression Severity Using Deep Autoencoding193 cited
  • 2013Vocal biomarkers of depression based on motor incoordination189 cited
  • 2016Multimodal and Multiresolution Depression Detection from Speech and Facial Landmark Features146 cited
  • 2015Automated Depression Diagnosis Based on Facial Dynamic Analysis and Sparse Coding141 cited

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  • 2014Psychiatric diagnosis: impersonal, imperfect and important113 cited
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    • 2018Integrating Deep and Shallow Models for Multi-Modal Depression Analysis—Hybrid Architectures119 cited
    • 2020A Deep Multiscale Spatiotemporal Network for Assessing Depression From Facial Dynamics116 cited
    • 2022Semi-Structural Interview-Based Chinese Multimodal Depression Corpus Towards Automatic Preliminary Screening of Depressive Disorders112 cited
    • 2020Depression Level Prediction Using Deep Spatiotemporal Features and Multilayer Bi-LTSM110 cited
    • 2019A Multi-Modal Hierarchical Recurrent Neural Network for Depression Detection105 cited
    • 2019Combining Global and Local Convolutional 3D Networks for Detecting Depression from Facial Expressions104 cited
    • 2023Unlocking the Emotional World of Visual Media: An Overview of the Science, Research, and Impact of Understanding Emotion89 cited
    • 2021End-to-end multimodal clinical depression recognition using deep neural networks: A comparative analysis88 cited
    • 2020Interpretation of Depression Detection Models via Feature Selection Methods86 cited
    • 2024Harnessing multimodal approaches for depression detection using large language models and facial expressions82 cited
    • 2020AudVowelConsNet: A phoneme-level based deep CNN architecture for clinical depression diagnosis81 cited
    • 2020Facial Depression Recognition by Deep Joint Label Distribution and Metric Learning80 cited
    • 2020Hybrid CNN-SVM classifier for efficient depression detection system73 cited
    • 2020Encoding Temporal Information For Automatic Depression Recognition From Facial Analysis72 cited
    • 2022E-Prevention: Advanced Support System for Monitoring and Relapse Prevention in Patients with Psychotic Disorders Analyzing Long-Term Multimodal Data from Wearables and Video Captures68 cited
    • 2024Multimodal Sensing for Depression Risk Detection: Integrating Audio, Video, and Text Data68 cited