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.

研究表明，语言使用与抑郁症相关，但需要大规模验证。本研究使用 Whispter 工具和 BERTopic 模型从 265 名参与者的 3919 个智能手机采集的语音记录中鉴定出 29 个话题。其中六个话题中 PHQ-8 中值大于等于 10 被视为抑郁风险话题：无期望、睡眠、心理疗法、剪发、学习和课程作业。我们比较了识别到的话题之间的行为（通过可穿戴设备）和语言特征，揭示了话题的产生及其与抑郁症的关联。同时，通过调查话题转变与时间内抑郁症严重程度的变化之间的关系，证明了长期监测语言使用的重要性。此外，我们还在一个类似较小的数据集上测试了 BERTopic 模型（57 名参与者的 356 个语音记录），获得了一些一致的结果。总结起来，我们的发现表明特定的语音话题可能会指示抑郁症的严重程度。所呈现的数据驱动工作流提供了一种在真实环境中收集和分析大规模语音数据的实用方法，可用于数字健康研究。

利用自动语音识别系统和深度学习主题模型在智能手机收集的自由回答语音记录中识别与抑郁相关的主题

Identifying depression-related topics in smartphone-collected  free-response speech recordings using an automatic speech recognition system  and a deep learning topic model