Effectiveness of Voice Quality Features in Detecting DepressionAmber Afshan, Jinxi Guo, Vijay Ravi, Jonathan Flint C, Abeer A. Alwan2018-08-28otherInterspeech (USB)78 citationsclosedNo readable copy stored yet — this record is metadata only.Emotion and Mood RecognitionVoice and Speech DisordersMental Health via WritingAbstractNo abstract was published for this record.Citations in this libraryStored only when both papers are in the corpus — 78 citations exist upstream.References2009The effects of psychotherapy for adult depression are overestimated: a meta-analysis of study quality and effect size2017AVEC 20172012Vocal Acoustic Biomarkers of Depression Severity and Treatment Response2011An investigation of depressed speech detection: features and normalization2016Multimodal and Multiresolution Depression Detection from Speech and Facial Landmark Features2013Detecting depression: A comparison between spontaneous and read speechCited by2019Automated speech-based screening of depression using deep convolutional neural networks2023Bio-acoustic features of depression: A review2020Detection of major depressive disorder using vocal acoustic analysis and machine learning—an exploratory study