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Multimodal Measurement of Depression Using Deep Learning Models
13 references·16 citing·49 second hop
Multimodal Measurement of Depression Using Deep Learning Models (2017)Multimodal Measurement of Depression Usin…AVEC 2017 (2017)AVEC 2017Automated Depression Diagnosis Based on Deep Networks to Encode Facial Appearance and Dynamics (2017)Automated Depression Diagnosi…Video-based emotion recognition using CNN-RNN and C3D hybrid networks (2016)Video-based emotion recogniti…Detecting Depression using Vocal, Facial and Semantic Communication Cues (2016)Detecting Depression using Vo…DepAudioNet (2016)DepAudioNetThe Geneva Minimalistic Acoustic Parameter Set (GeMAPS) for Voice Research and Affective Computing (2015)The Geneva Minimalistic Acous…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…Deep learning for robust feature generation in audiovisual emotion recognition (2013)Deep learning for robust feat…Emotion recognition from speech: a review (2012)Emotion recognition from spee…An investigation of depressed speech detection: features and normalization (2011)An investigation of depressed…Mental health, resilience and inequalities (2009)Mental health, resilience and…Detecting depression from facial actions and vocal prosody (2009)Detecting depression from fac…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…Depression Detection in Speech Using Transformer and Parallel Convolutional Neural Networks (2023)Depression Detection in Speec…Semi-Structural Interview-Based Chinese Multimodal Depression Corpus Towards Automatic Preliminary Screening of Depressive Disorders (2022)Semi-Structural Interview-Bas…Deep learning techniques for suicide and depression detection from online social media: A scoping review (2022)Deep learning techniques for …D-vlog: Multimodal Vlog Dataset for Depression Detection (2022)D-vlog: Multimodal Vlog Datas…Automatic depression recognition by intelligent speech signal processing: A systematic survey (2022)Automatic depression recognit…A hybrid model for depression detection using deep learning (2022)A hybrid model for depression…End-to-end multimodal clinical depression recognition using deep neural networks: A comparative analysis (2021)End-to-end multimodal clinica…Towards Automatic Depression Detection: A BiLSTM/1D CNN-Based Model (2020)Towards Automatic Depression …AudVowelConsNet: A phoneme-level based deep CNN architecture for clinical depression diagnosis (2020)AudVowelConsNet: A phoneme-le…Multitask Representation Learning for Multimodal Estimation of Depression Level (2019)Multitask Representation Lear…Context-aware Deep Learning for Multi-modal Depression Detection (2019)Context-aware Deep Learning f…Combining Global and Local Convolutional 3D Networks for Detecting Depression from Facial Expressions (2019)Combining Global and Local Co…Automatic Assessment of Depression From Speech via a Hierarchical Attention Transfer Network and Attention Autoencoders (2019)Automatic Assessment of Depre…Integrating Deep and Shallow Models for Multi-Modal Depression Analysis—Hybrid Architectures (2018)Integrating Deep and Shallow …Toward detecting emotions in spoken dialogs (2005)IEMOCAP: interactive emotional dyadic motion capture database (2008)Survey on speech emotion recognition: Features, classification schemes, and databases (2010)Hierarchical Convolutional Neural Networks for EEG-Based Emotion Recognition (2017)Learning Affective Features With a Hybrid Deep Model for Audio–Visual Emotion Recognition (2017)Speech Emotion Recognition From 3D Log-Mel Spectrograms With Deep Learning Network (2019)Vocal communication of emotion: A review of research paradigms (2003)A database of German emotional speech (2005)Affective computing in virtual reality: emotion recognition from brain and heartbeat dynamics using wearable sensors (2018)Databases, features and classifiers for speech emotion recognition: a review (2018)Speech emotion recognition (2018)Handbook of Positive Psychology (2001)On Happiness and Human Potentials: A Review of Research on Hedonic and Eudaimonic Well-Being (2001)The world report on violence and health (2002)Mental health and development: targeting people with mental health conditions as a vulnerable group. (2010)Mental Health Promotion in Public Health: Perspectives and Strategies From Positive Psychology (2011)Resilience and responsibility: governing uncertainty in a complex world (2013)A RATING SCALE FOR DEPRESSION (1960)Diagnostic and Statistical Manual of Mental Disorders (2008)Diagnostic and Statistical Manual of Mental Disorders (2013)AVEC 2014 (2014)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)Acoustic profiles in vocal emotion expression. (1996)Praat, a system for doing phonetics by computer (2002)Adieu features? 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References

  • 2015The Geneva Minimalistic Acoustic Parameter Set (GeMAPS) for Voice Research and Affective Computing1,815 cited
  • 2012Emotion recognition from speech: a review644 cited
  • 2014The Distress Analysis Interview Corpus of human and computer interviews591 cited
  • 2016Video-based emotion recognition using CNN-RNN and C3D hybrid networks557 cited
  • 2009Detecting depression from facial actions and vocal prosody494 cited
  • 2009Mental health, resilience and inequalities475 cited
  • 2013Deep learning for robust feature generation in audiovisual emotion recognition401 cited
  • 2017AVEC 2017332 cited
  • 2016DepAudioNet299 cited
  • 2017Automated Depression Diagnosis Based on Deep Networks to Encode Facial Appearance and Dynamics260 cited
  • 2016Detecting Depression using Vocal, Facial and Semantic Communication Cues218 cited
  • 2014Vocal and Facial Biomarkers of Depression based on Motor Incoordination and Timing209 cited
  • 2011An investigation of depressed speech detection: features and normalization188 cited

Cited by

  • 2022A hybrid model for depression detection using deep learning164 cited
  • 2024Machine Learning for Multimodal Mental Health Detection: A Systematic Review of Passive Sensing Approaches

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153 cited
  • 2019Context-aware Deep Learning for Multi-modal Depression Detection129 cited
  • 2022D-vlog: Multimodal Vlog Dataset for Depression Detection126 cited
  • 2018Integrating Deep and Shallow Models for Multi-Modal Depression Analysis—Hybrid Architectures119 cited
  • 2019Multitask Representation Learning for Multimodal Estimation of Depression Level115 cited
  • 2022Semi-Structural Interview-Based Chinese Multimodal Depression Corpus Towards Automatic Preliminary Screening of Depressive Disorders112 cited
  • 2019Combining Global and Local Convolutional 3D Networks for Detecting Depression from Facial Expressions104 cited
  • 2019Automatic Assessment of Depression From Speech via a Hierarchical Attention Transfer Network and Attention Autoencoders104 cited
  • 2020Towards Automatic Depression Detection: A BiLSTM/1D CNN-Based Model100 cited
  • 2022Automatic depression recognition by intelligent speech signal processing: A systematic survey99 cited
  • 2022Deep learning techniques for suicide and depression detection from online social media: A scoping review94 cited
  • 2021End-to-end multimodal clinical depression recognition using deep neural networks: A comparative analysis88 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
  • 2023Depression Detection in Speech Using Transformer and Parallel Convolutional Neural Networks75 cited