Barriers to accessing mental health assessments including cost and stigma continues to be an impediment in mental health diagnosis and treatment. Machine learning approaches based on speech samples could help in this direction. In this work, we develop machine learning solutions to diagnose anxiety disorders from audio journals of patients. We work on a novel anxiety dataset (provided through collaboration with Kintsugi Mindful Wellness Inc.) and experiment with several models of varying complexity utilizing audio, text and a combination of multiple modalities. We show that the multi-modal and audio embeddings based approaches achieve good performance in the task achieving an AUC ROC score of 0.68-0.69.

翻译：获取心理健康评估的障碍（包括费用和污名化）持续阻碍着心理疾病的诊断与治疗。基于语音样本的机器学习方法可能为此提供助力。本研究开发了从患者音频日志中诊断焦虑障碍的机器学习解决方案。我们基于新型焦虑数据集（通过与Kintsugi Mindful Wellness Inc.合作提供）开展研究，运用音频、文本及多模态组合等多种复杂度模型进行实验。结果表明，基于多模态和音频嵌入的方法在该任务中表现良好，AUC ROC评分达到0.68-0.69。