The Distress Analysis Interview Corpus of human and computer interviews

By Jonathan Gratch, Ron Artstein, Gale M. Lucas, Giota Stratou, Stefan Scherer, Angela Nazarian, Rachel Wood, Jill Boberg, David DeVault, Stacy C. Marsella and 3 more. International Conference on Language Resources and Evaluation, 2014

840 citations recorded by the source corpus.

The Distress Analysis Interview Corpus (DAIC) contains clinical interviews designed to support the diagnosis of psychological distress conditions such as anxiety, depression, and post traumatic stress disorder. The interviews are conducted by humans, human controlled agents and autonomous agents, and the participants include both distressed and non-distressed individuals. Data collected include audio and video recordings and extensive questionnaire responses; parts of the corpus have been transcribed and annotated for a variety of verbal and non-verbal features. The corpus has been used to support the creation of an automated interviewer agent, and for research on the automatic identification of psychological distress.

Subfields: Clinical Psychology, Social Psychology

View Paper: https://www.semanticscholar.org/paper/ebb4c2e0cb5c5aa0ccdf8882f3607c79f3b00fe5

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Citations in the corpus

SimSensei kiosk: a virtual human interviewer for healthcare decision support (2014, methodology): “In the first cycle, a corpus of 120 face-to-face interactions between a confederate interviewer and a paid participant was collected [9, 31, 10].”
Detecting Depression with Audio/Text Sequence Modeling of Interviews (2018, methodology): “The data was split into a training (57%, 107 subjects), development (19%, 35 subjects), and test (25%, 47 subjects) sets as speciﬁed by [21, 20].”
AVEC 2016: Depression, Mood, and Emotion Recognition Workshop and Challenge (2016, methodology): “The Distress Analysis Interview Corpus - Wizard of Oz (DAIC-WOZ) database is part of a larger corpus, the Distress Analysis Interview Corpus (DAIC) [17], that contains clinical interviews designed to support the diagnosis of psychological distress conditions such as anxiety, depression, and…”
AVEC 2019 Workshop and Challenge: State-of-Mind, Detecting Depression with AI, and Cross-Cultural Affect Recognition (2019, methodology): “The AVEC 2019 Challenge relies on three corpora: (i) the USoM corpus [46] for the SoMS, (ii) the Extended-DAIC corpus [23] for the DDS, and (iii) the SEWA dataset [31] for the CES.”
DepAudioNet: An Efficient Deep Model for Audio based Depression Classification (2016, background): “DAIC-WOZ is part of a large corpus, namely the Distress Analysis Interview Corpus (DAIC) [14], which contains clinical interviews designed to support the diagnosis of psychological distress conditions, such as anxiety, depression and post-traumatic stress disorder.”
Automated assessment of psychiatric disorders using speech: A systematic review (2019, background): “65 Another approach consists of capturing audio on the participant's device, extracting encrypted acoustic features from which the raw audio cannot be reconstructed, and then sending the features to a secure server to later download.”