Language use has been shown to correlate with depression, but large-scale validation is needed. Traditional methods like clinic studies are expensive. So, natural language processing has been employed on social media to predict depression, but limitations remain-lack of validated labels, biased user samples, and no context. Our study identified 29 topics in 3919 smartphone-collected speech recordings from 265 participants using the Whisper tool and BERTopic model. Six topics with a median PHQ-8 greater than or equal to 10 were regarded as risk topics for depression: No Expectations, Sleep, Mental Therapy, Haircut, Studying, and Coursework. To elucidate the topic emergence and associations with depression, we compared behavioral (from wearables) and linguistic characteristics across identified topics. The correlation between topic shifts and changes in depression severity over time was also investigated, indicating the importance of longitudinally monitoring language use. We also tested the BERTopic model on a similar smaller dataset (356 speech recordings from 57 participants), obtaining some consistent results. In summary, our findings demonstrate specific speech topics may indicate depression severity. The presented data-driven workflow provides a practical approach to collecting and analyzing large-scale speech data from real-world settings for digital health research.

翻译：语言使用已被证明与抑郁存在关联，但尚需大规模验证。传统研究方法如临床研究成本高昂。为此，自然语言处理技术已被应用于社交媒体开展抑郁预测，但仍存在局限性——缺乏经验证的标签、样本存在用户偏差、缺乏上下文信息。本研究利用Whisper工具和BERTopic模型，从265名参与者通过智能手机采集的3919条语音记录中识别出29个主题。其中6个中位PHQ-8评分≥10的主题被认定为抑郁风险主题：无所谓期待、睡眠、心理治疗、理发、学习、课程作业。为阐明主题产生机制及其与抑郁的关联，我们比较了不同主题间（来自可穿戴设备的）行为特征与语言特征，并探究了主题转换与抑郁严重程度随时间变化的关联，揭示了纵向监测语言使用的重要性。我们还在类似的小规模数据集（57名参与者的356条语音记录）上测试了BERTopic模型，获得部分一致性结果。综上，本研究发现特定语音主题可指示抑郁严重程度。所提出的数据驱动工作流为数字健康研究中采集与分析真实场景下的大规模语音数据提供了实用方法。