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

Yuezhou Zhang; Amos A Folarin; Judith Dineley; Pauline Conde; Valeria de Angel; Shaoxiong Sun; Yatharth Ranjan; Zulqarnain Rashid; Callum Stewart; Petroula Laiou; Heet Sankesara; Linglong Qian; Faith Matcham; Katie M White; Carolin Oetzmann; Femke Lamers; Sara Siddi; Sara Simblett; Björn W. Schuller; Srinivasan Vairavan; Til Wykes; Josep Maria Haro; Brenda WJH Penninx; Vaibhav A Narayan; Matthew Hotopf; Richard JB Dobson; Nicholas Cummins; RADAR-CNS consortium

arXiv · 2308.11773

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

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Abstract

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.

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Yuezhou Zhang, Amos A Folarin, Judith Dineley, Pauline Conde, Valeria de Angel, Shaoxiong Sun, Yatharth Ranjan, Zulqarnain Rashid, Callum Stewart, Petroula Laiou, Heet Sankesara, Linglong Qian, Faith Matcham, Katie M White, Carolin Oetzmann, Femke Lamers, Sara Siddi, Sara Simblett, Björn W. Schuller, Srinivasan Vairavan, Til Wykes, Josep Maria Haro, Brenda WJH Penninx, Vaibhav A Narayan, Matthew Hotopf, Richard JB Dobson, Nicholas Cummins, RADAR-CNS consortium. 2023-09-05. Identifying depression-related topics in smartphone-collected free-response speech recordings using an automatic speech recognition system and a deep learning topic model. https://arxiv.org/abs/2308.11773

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