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Detecting Depression from Voice
11 references·0 citing·51 second hopsampled
Detecting Depression from Voice (2019)Detecting Depression from VoiceAutomated depression analysis using convolutional neural networks from speech (2018)Automated depression analysis…Topic Modeling Based Multi-modal Depression Detection (2017)Topic Modeling Based Multi-mo…Hybrid Depression Classification and Estimation from Audio Video and Text Information (2017)Hybrid Depression Classificat…AVEC 2017 (2017)AVEC 2017A Random Forest Regression Method With Selected-Text Feature For Depression Assessment (2017)A Random Forest Regression Me…StudentLife (2014)StudentLifeVocal biomarkers of depression based on motor incoordination (2013)Vocal biomarkers of depressio…Modeling spectral variability for the classification of depressed speech (2013)Modeling spectral variability…AVEC 2013 (2013)AVEC 2013Detection of Clinical Depression in Adolescents’ Speech During Family Interactions (2010)Detection of Clinical Depress…Critical Analysis of the Impact of Glottal Features in the Classification of Clinical Depression in Speech (2007)Critical Analysis of the Impa…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 2014 (2014)EEG Based Emotion Recognition: A Tutorial and Review (2022)Acoustical properties of speech as indicators of depression and suicidal risk (2000)Detecting depression from facial actions and vocal prosody (2009)Decision Tree Based Depression Classification from Audio Video and Language Information (2016)Video-Based Depression Level Analysis by Encoding Deep Spatiotemporal Features (2018)Spectral Representation of Behaviour Primitives for Depression Analysis (2020)Vocal affect expression: A review and a model for future research. (1986)Emotion recognition in human-computer interaction (2001)Prevalence, Severity, and Comorbidity of 12-Month DSM-IV Disorders in the National Comorbidity Survey Replication (2005)An investigation of depressed speech detection: features and normalization (2011)Speech emotion recognition approaches in human computer interaction (2011)Multimodal assistive technologies for depression diagnosis and monitoring (2013)The emergence of gender differences in depression during adolescence. (1994)Major depressive disorder in older adolescents (1998)A survey of speech emotion recognition in natural environment (2020)A hybrid model for depression detection using deep learning (2022)Emotion recognition from unimodal to multimodal analysis: A review (2023)A Global Measure of Perceived Stress (1983)Development and validation of brief measures of positive and negative affect: The PANAS scales. (1988)The PHQ-9 (2001)Mobile Phone Sensor Correlates of Depressive Symptom Severity in Daily-Life Behavior: An Exploratory Study (2015)Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning (2017)Mental Health and Behavior of College Students During the Early Phases of the COVID-19 Pandemic: Longitudinal Smartphone and Ecological Momentary Assessment Study (2020)A Concordance Correlation Coefficient to Evaluate Reproducibility (1989)The PHQ-8 as a measure of current depression in the general population (2008)The Geneva Minimalistic Acoustic Parameter Set (GeMAPS) for Voice Research and Affective Computing (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)IEMOCAP: interactive emotional dyadic motion capture database (2008)Empirical study of topic modeling in Twitter (2010)A Depression Recognition Method for College Students Using Deep Integrated Support Vector Algorithm (2020)Machine Learning for Multimodal Mental Health Detection: A Systematic Review of Passive Sensing Approaches (2024)The Distress Analysis Interview Corpus of human and computer interviews (2014)Predicting Depression via Social Media (2021)Depression Detection in Speech Using Transformer and Parallel Convolutional Neural Networks (2023)Spatial–Temporal Feature Network for Speech-Based Depression Recognition (2023)Harnessing multimodal approaches for depression detection using large language models and facial expressions (2024)AVEC 2016 (2016)AudVowelConsNet: A phoneme-level based deep CNN architecture for clinical depression diagnosis (2020)End-to-end multimodal clinical depression recognition using deep neural networks: A comparative analysis (2021)A deep learning model for depression detection based on MFCC and CNN generated spectrogram features (2023)A RATING SCALE FOR DEPRESSION (1960)A New Depression Scale Designed to be Sensitive to Change (1979)Diagnostic and Statistical Manual of Mental Disorders (2013)A Novel Approach for Classification of Speech Emotions Based on Deep and Acoustic Features (2020)Multimodal Spatiotemporal Representation for Automatic Depression Level Detection (2020)Transformer-based multimodal feature enhancement networks for multimodal depression detection integrating video, audio and remote photoplethysmograph signals (2023)
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References

  • 2014StudentLife1,117 cited
  • 2013AVEC 2013584 cited
  • 2017AVEC 2017332 cited
  • 2010Detection of Clinical Depression in Adolescents’ Speech During Family Interactions256 cited
  • 2018Automated depression analysis using convolutional neural networks from speech250 cited
  • 2007Critical Analysis of the Impact of Glottal Features in the Classification of Clinical Depression in Speech220 cited
  • 2013Vocal biomarkers of depression based on motor incoordination189 cited
  • 2017Topic Modeling Based Multi-modal Depression Detection155 cited
  • 2017Hybrid Depression Classification and Estimation from Audio Video and Text Information98 cited
  • 2017A Random Forest Regression Method With Selected-Text Feature For Depression Assessment88 cited
  • 2013Modeling spectral variability for the classification of depressed speech53 cited

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