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.

翻译：抑郁和焦虑等心理困扰在全球疾病负担中占比最大。得益于人工智能领域的创新，这类疾病的自动诊断系统可减轻患者痛苦，但其开发需要信息丰富且均衡的语料库。本文提出一种新型心理困扰分析音频数据集DEPAC，该数据集基于抑郁和焦虑标准筛查工具的既定阈值进行标注。这一大规模数据集包含每位受试者的多种语音任务及相关人口统计学信息。同时，我们提出一组由人工精选的声学和语言学特征集，该特征集已被证明可有效识别人类语音中的精神疾病体征。最后，通过比较基于该数据集构建的基线机器学习模型与基于其他已知抑郁语料库训练的基线模型在预测抑郁严重程度方面的性能，我们验证了所提音频语料库和特征集的质量与有效性。