DEPAC: a Corpus for Depression and Anxiety Detection from Speech

TL;DR

This paper introduces DEPAC, a comprehensive speech dataset for depression and anxiety detection, along with effective features and baseline models demonstrating its utility in mental health assessment.

Contribution

The work presents a novel, well-annotated speech corpus and a curated feature set, advancing automated mental health diagnosis tools.

Findings

Baseline models perform better on DEPAC than on other corpora.

The dataset includes diverse speech tasks and demographic data.

Effective acoustic and linguistic features are identified for depression and anxiety detection.

Abstract

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.