DEPAC: a Corpus for Depression and Anxiety Detection from Speech
Mashrura Tasnim Malikeh Ehghaghi Brian Diep Jekaterina Novikova
Mental distress like depression and anxiety contribute to the largest
proportion of the global burden of diseases. Automated diagnosis systems of
such disorders, empowered by recent innovations in Artificial Intelligence, can
pave the way to reduce the sufferings of the affected individuals. Development
of such systems requires information-rich and balanced corpora. In this work,
we introduce a novel mental distress analysis audio dataset DEPAC, labeled
based on established thresholds on depression and anxiety standard screening
tools. This large dataset comprises multiple speech tasks per individual, as
well as relevant demographic information. Alongside, we present a feature set
consisting of hand-curated acoustic and linguistic features, which were found
effective in identifying signs of mental illnesses in human speech. Finally, we
justify the quality and effectiveness of our proposed audio corpus and feature
set in predicting depression severity by comparing the performance of baseline
machine learning models built on this dataset with baseline models trained on
other well-known depression corpora.