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Automatic Depression Detection: an Emotional Audio-Textual Corpus and A Gru/Bilstm-Based Model
9 references·11 citing·48 second hop
Automatic Depression Detection: an Emotional Audio-Textual Corpus and A Gru/Bilstm-Based Model (2022)Automatic Depression Detection: an Emotio…Detecting Depression with Audio/Text Sequence Modeling of Interviews (2018)Detecting Depression with Aud…Detecting Depression using Vocal, Facial and Semantic Communication Cues (2016)Detecting Depression using Vo…DepAudioNet (2016)DepAudioNetThe Distress Analysis Interview Corpus of human and computer interviews (2014)The Distress Analysis Intervi…AVEC 2014 (2014)AVEC 2014Detecting Depression Severity from Vocal Prosody (2013)Detecting Depression Severity…AVEC 2013 (2013)AVEC 2013The Costs of Depression (2011)The Costs of DepressionA Self-Rating Depression Scale (1965)A Self-Rating Depression ScaleEffectiveness Evaluation for Clinical Depression Detection Using Deep Learning Based Synthetic House-Tree-Person Test (2025)Effectiveness Evaluation for …Multimodal Sensing for Depression Risk Detection: Integrating Audio, Video, and Text Data (2024)Multimodal Sensing for Depres…Machine Learning for Multimodal Mental Health Detection: A Systematic Review of Passive Sensing Approaches (2024)Machine Learning for Multimod…Harnessing multimodal approaches for depression detection using large language models and facial expressions (2024)Harnessing multimodal approac…Additive Cross-Modal Attention Network (ACMA) for Depression Detection Based on Audio and Textual Features (2024)Additive Cross-Modal Attentio…A comprehensive review of predictive analytics models for mental illness using machine learning algorithms (2024)A comprehensive review of pre…Machine learning based approaches for clinical and non-clinical depression recognition and depression relapse prediction using audiovisual and EEG modalities: A comprehensive review (2023)Machine learning based approa…IIFDD: Intra and inter-modal fusion for depression detection with multi-modal information from Internet of Medical Things (2023)IIFDD: Intra and inter-modal …Attention guided learnable time-domain filterbanks for speech depression detection (2023)Attention guided learnable ti…TAMFN: Time-Aware Attention Multimodal Fusion Network for Depression Detection (2022)TAMFN: Time-Aware Attention M…Semi-Structural Interview-Based Chinese Multimodal Depression Corpus Towards Automatic Preliminary Screening of Depressive Disorders (2022)Semi-Structural Interview-Bas…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)AVEC 2016 (2016)EEG Based Emotion Recognition: A Tutorial and Review (2022)Psychiatric disorders in America: the epidemiologic catchment area study (1991)Cross-National Epidemiology of Major Depression and Bipolar Disorder (1996)Depression, chronic diseases, and decrements in health: results from the World Health Surveys (2007)The efficacy of short-term psychodynamic psychotherapy for depression: A meta-analysis update (2015)Prevalence and correlates of major depressive disorder: a systematic review (2020)Biological, Psychological, and Social Determinants of Depression: A Review of Recent Literature (2021)A RATING SCALE FOR DEPRESSION (1960)Prevalence, Severity, and Comorbidity of 12-Month DSM-IV Disorders in the National Comorbidity Survey Replication (2005)Diagnostic and Statistical Manual of Mental Disorders (2013)Automated Depression Diagnosis Based on Deep Networks to Encode Facial Appearance and Dynamics (2017)Automated depression analysis using convolutional neural networks from speech (2018)Visually Interpretable Representation Learning for Depression Recognition from Facial Images (2018)An Inventory for Measuring Depression (1961)Development and validation of a geriatric depression screening scale: A preliminary report (1982)Happiness is everything, or is it? Explorations on the meaning of psychological well-being. (1989)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)SimSensei kiosk: a virtual human interviewer for healthcare decision support (2014)Automated assessment of psychiatric disorders using speech: A systematic review (2020)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)Deep learning for depression recognition with audiovisual cues: A review (2021)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)Multimodal Measurement of Depression Using Deep Learning Models (2017)Predicting Depression via Social Media (2021)A hybrid model for depression detection using deep learning (2022)A multimodal fusion model with multi-level attention mechanism for depression detection (2022)Screening for Depression in Medical Settings with the Patient Health Questionnaire (PHQ): A Diagnostic Meta-Analysis (2007)Burden of Depressive Disorders by Country, Sex, Age, and Year: Findings from the Global Burden of Disease Study 2010 (2013)Algorithm Aversion: People Erroneously Avoid Algorithms after Seeing Them Err (2014)SenseMood: Depression Detection on Social Media (2020)Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement (2009)Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement (2009)Diagnostic and Statistical Manual of Mental Disorders (2013)Mental Health Management Through Wearables and AI Innovation (2025)Passive Sensing for Mental Health Monitoring Using Machine Learning With Wearables and Smartphones: Scoping Review (2025)From Tool to Agent: A Semi-Systematic Review of Human–AI Alignment and a Proposed Tiered Healing Ecosystem for Mental Health (2026)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

  • 1965A Self-Rating Depression Scale9,808 cited
  • 2011The Costs of Depression871 cited
  • 2014The Distress Analysis Interview Corpus of human and computer interviews591 cited
  • 2013AVEC 2013584 cited
  • 2014AVEC 2014475 cited
  • 2018Detecting Depression with Audio/Text Sequence Modeling of Interviews350 cited
  • 2016DepAudioNet299 cited
  • 2013Detecting Depression Severity from Vocal Prosody278 cited
  • 2016Detecting Depression using Vocal, Facial and Semantic Communication Cues218 cited

Cited by

  • 2024Machine Learning for Multimodal Mental Health Detection: A Systematic Review of Passive Sensing Approaches153 cited
  • 2022Semi-Structural Interview-Based Chinese Multimodal Depression Corpus Towards Automatic Preliminary Screening of Depressive Disorders112 cited
  • 2023IIFDD: Intra and inter-modal fusion for depression detection with multi-modal information from Internet of Medical Things99 cited
  • 2023Machine learning based approaches for clinical and non-clinical depression recognition and depression relapse prediction using audiovisual and EEG modalities: A comprehensive review90 cited
  • 2024Harnessing multimodal approaches for depression detection using large language models and facial expressions82 cited

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  • 2022TAMFN: Time-Aware Attention Multimodal Fusion Network for Depression Detection78 cited
  • 2023Attention guided learnable time-domain filterbanks for speech depression detection69 cited
  • 2024Multimodal Sensing for Depression Risk Detection: Integrating Audio, Video, and Text Data68 cited
  • 2024A comprehensive review of predictive analytics models for mental illness using machine learning algorithms68 cited
  • 2024Additive Cross-Modal Attention Network (ACMA) for Depression Detection Based on Audio and Textual Features52 cited
  • 2025Effectiveness Evaluation for Clinical Depression Detection Using Deep Learning Based Synthetic House-Tree-Person Test3 cited