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Tracking depression severity from audio and video based on speech articulatory coordination
16 references·3 citing·49 second hop
Tracking depression severity from audio and video based on speech articulatory coordination (2018)Tracking depression severity from audio a…Detecting Depression using Vocal, Facial and Semantic Communication Cues (2016)Detecting Depression using Vo…Self-Reported Symptoms of Depression and PTSD Are Associated with Reduced Vowel Space in Screening Interviews (2015)Self-Reported Symptoms of Dep…Analysis of acoustic space variability in speech affected by depression (2015)Analysis of acoustic space va…A review of depression and suicide risk assessment using speech analysis (2015)A review of depression and su…Vocal and Facial Biomarkers of Depression based on Motor Incoordination and Timing (2014)Vocal and Facial Biomarkers o…AVEC 2014 (2014)AVEC 2014Vocal biomarkers of depression based on motor incoordination (2013)Vocal biomarkers of depressio…The computer expression recognition toolbox (CERT) (2011)The computer expression recog…The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression (2010)The Extended Cohn-Kanade Data…Influence of acoustic low-level descriptors in the detection of clinical depression in adolescents (2010)Influence of acoustic low-lev…Diagnostic and Statistical Manual of Mental Disorders (DSM) (2009)Diagnostic and Statistical Ma…Voice acoustic measures of depression severity and treatment response collected via interactive voice response (IVR) technology (2006)Voice acoustic measures of de…Investigation of Vocal Jitter and Glottal Flow Spectrum as Possible Cues for Depression and Near-Term Suicidal Risk (2004)Investigation of Vocal Jitter…Acoustical properties of speech as indicators of depression and suicidal risk (2000)Acoustical properties of spee…Facial signs of emotional experience. (1980)Facial signs of emotional exp…Facial Expression and Imagery in Depression: An Electromyographic Study (1976)Facial Expression and Imagery…Bio-acoustic features of depression: A review (2023)Bio-acoustic features of depr…Real-time Acoustic based Depression Detection using Machine Learning Techniques (2020)Real-time Acoustic based Depr…MODMA dataset: a Multi-modal Open Dataset for Mental-disorder Analysis (2020)MODMA dataset: a Multi-modal …Facial Action Coding System (1978)Comprehensive database for facial expression analysis (2002)Categorical and dimensional affect analysis in continuous input: Current trends and future directions (2012)AVEC 2016 (2016)Facial emotion recognition using convolutional neural networks (FERC) (2020)The PHQ-9 (2001)Diagnostic and Statistical Manual of Mental Disorders (2013)Diagnostic and Statistical Manual of Mental Disorders (2013)AVEC 2017 (2017)AVEC 2019 Workshop and Challenge: State-of-Mind, Detecting Depression with AI, and Cross-Cultural Affect Recognition (2019)Automated assessment of psychiatric disorders using speech: A systematic review (2020)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)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)A RATING SCALE FOR DEPRESSION (1960)An Inventory for Measuring Depression (1961)The Hospital Anxiety and Depression Scale (1983)Vocal Acoustic Biomarkers of Depression Severity and Treatment Response (2012)Detecting Depression Severity from Vocal Prosody (2013)Nonverbal Communication (2018)The expression of the emotions in man and animals. 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(1986)Emotional speech: Towards a new generation of databases (2003)Features and classifiers for emotion recognition from speech: a survey from 2000 to 2011 (2012)Speech Emotion Recognition from Spectrograms with Deep Convolutional Neural Network (2017)Durational variability in speech and the Rhythm Class Hypothesis (2002)The PHQ-8 as a measure of current depression in the general population (2008)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)Multimodal Depression Detection: Fusion Analysis of Paralinguistic, Head Pose and Eye Gaze Behaviors (2016)Dynamic Multimodal Measurement of Depression Severity Using Deep Autoencoding (2017)Multimodal Measurement of Depression Using Deep Learning Models (2017)AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild (2017)Reliable Crowdsourcing and Deep Locality-Preserving Learning for Expression Recognition in the Wild (2017)Deep Facial Expression Recognition: A Survey (2020)How to evaluate metacognitive functioning in psychotherapy? The metacognition assessment scale and its applications (2003)The neuropsychology of adult obsessive–compulsive disorder: A meta-analysis (2013)Intergenerational transmission and prevention of adverse childhood experiences (ACEs) (2021)
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References

  • 2010The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression4,299 cited
  • 2009Diagnostic and Statistical Manual of Mental Disorders (DSM)2,120 cited
  • 2015A review of depression and suicide risk assessment using speech analysis1,007 cited
  • 1980Facial signs of emotional experience.888 cited
  • 2011The computer expression recognition toolbox (CERT)552 cited
  • 2000Acoustical properties of speech as indicators of depression and suicidal risk504 cited
  • 2014AVEC 2014475 cited
  • 2006Voice acoustic measures of depression severity and treatment response collected via interactive voice response (IVR) technology380 cited
  • 2016Detecting Depression using Vocal, Facial and Semantic Communication Cues218 cited
  • 2014Vocal and Facial Biomarkers of Depression based on Motor Incoordination and Timing209 cited
  • 2004Investigation of Vocal Jitter and Glottal Flow Spectrum as Possible Cues for Depression and Near-Term Suicidal Risk204 cited
  • 1976Facial Expression and Imagery in Depression: An Electromyographic Study192 cited
  • 2013Vocal biomarkers of depression based on motor incoordination189 cited
  • 2015Self-Reported Symptoms of Depression and PTSD Are Associated with Reduced Vowel Space in Screening Interviews127 cited

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  • 2015Analysis of acoustic space variability in speech affected by depression121 cited
  • 2010Influence of acoustic low-level descriptors in the detection of clinical depression in adolescents78 cited
  • Cited by

    • 2023Bio-acoustic features of depression: A review63 cited
    • 2020Real-time Acoustic based Depression Detection using Machine Learning Techniques60 cited
    • 2020MODMA dataset: a Multi-modal Open Dataset for Mental-disorder Analysis58 cited